Auto Finance (and auto loans) contribute a significant chunk of the lending economy, comprising 25% of the non-mortgage accounts in the US. With lower rates, auto loans are expected to become more affordable, potentially boosting loan originations. Additionally, stable inflation may enhance car affordability for consumers.
While conditions seem to improve for Auto Finance and Buy-Here-Pay-Here consumers regarding loan originations, the high delinquency rates remain a significant concern. This article will explore why managing collections and reducing delinquencies on existing accounts will continue to be challenging for auto lenders.
In short, loans originated in 2022-23 were issued on high car prices and loan amounts, creating financial strain. Lower interest rates can’t offset these high monthly payments, which will likely drive delinquencies higher over the remaining loan terms.
Factors Contributing To High Delinquencies in Auto Loans
Auto loan delinquencies are currently 60% above pre-pandemic levels, reaching their highest point since the 2008 housing crisis. The 30-day past-due rate (DPD) now stands at 3.7%, with most delinquencies originating from near-prime or subprime borrowers. Notably, delinquency rates are rising sharply among loans issued in 2022-23.
The primary driver of these high delinquency rates is the high monthly loan payments, which surged by 27% from January 2020 to January 2023. In comparison, monthly payments only grew by 9.3% from January 2017 to January 2020. According to Federal Reserve research, a 1% rise in monthly loan payments increases the likelihood of delinquencies by 0.03%.
Monthly loan payments are influenced by three main factors:
Loan Value
Loan Tenure
Interest Rates
For simplicity, we’ll keep the loan tenure constant, as standard terms typically range between 60 to 66 months. Focusing on the other factors, subprime borrowers saw a 30% increase in loan value during 2022-23, coupled with a 140-basis-point rise in interest rates.
The table below illustrates how shifts in loan value and interest rates affect subprime borrowers. These figures assume typical market rates and loan amounts for this segment, reflecting post-pandemic trends in both parameters.
A 200-basis-point rise in interest rates (from Scenario A to B) leads to a 3% increase in monthly payments.
When loan value jumps by 25% (from Scenario B to C), monthly payments increase by 25%.
Combining these factors (Scenario C compared to A) results in a total 30% increase in monthly payments. Even if interest rates decline (Scenario D compared to C), monthly payments would only decrease by about 3%.
During the post-pandemic period, car prices rose significantly due to supply chain disruptions, chip shortages, and increased demand driven by low interest rates and economic stimulus. As a result, banks tightened lending standards, and most auto loans during this time were issued by credit unions, captive finance companies, BHPH dealerships, and auto finance firms. Rejection rates for auto loans, which were approximately 7% pre-pandemic, fell to around 5%. [Refer to “Trends in 2022-23” graphic below]
Unlike banks, it is credit unions, BHPH dealerships, and auto finance companies that are likely to experience a continued rise in delinquencies, especially from loans originated in 2022-23. Given the typical 5-year loan term, it may take an additional 2-3 years before delinquency rates return to the more acceptable, pre-pandemic benchmarks.
So, What Can We Do?
Consistent consumer engagement is essential for managing delinquencies effectively. Providing 24/7 support and flexible payment options can optimize recovery efforts. Reaching consumers through their preferred channels—whether voice, SMS, or email—enhances connectivity and engagement.
Skit.ai offers omnichannel capabilities, enabling seamless conversations across all communication channels. Leveraging generative AI specialized in collections, Skit.ai verifies consumers, negotiates terms, offers flexible payment plans, and processes payments—all without requiring agent intervention. The Conversational AI platform can place multiple calls simultaneously to increase contact attempts per account, and it can send and receive SMS responses from customers, processing payments via credit cards directly through SMS.
Are you interested in learning more about how Conversational AI can benefit your business? Book a demo with one of our experts.
“Science is the search for truth, that is, the effort to understand the world: it involves the rejection of bias, of dogma, of revelation, but not the rejection of morality.”– Linus Pauling
An Introduction to Bias in AI
Artificial Intelligence has transformed industries worldwide, reshaping how businesses operate, governments function, and individuals interact with technology. From healthcare and finance to retail and law enforcement, AI is driving efficiency, innovation, and decision-making at an unprecedented scale. However, as AI continues to integrate into society, its growing influence has also brought ethical concerns to the forefront, particularly around the issue of bias in AI.
AI, at its core, is designed to replicate or simulate human intelligence, but the data it processes is often tainted by human prejudices, leading to skewed or biased outcomes. This raises ethical questions about the role AI should play in shaping critical aspects of life, including employment, justice, healthcare, and security. The ethical issues in AI are not merely theoretical—they have tangible consequences that affect real people and institutions.
In this blog post, we will explore the origins of bias in AI, its far-reaching consequences, and strategies for mitigating these biases. We will also dive into the ethical boundaries of AI usage, current efforts to address bias, and the evolving landscape of AI ethics.
Bias in AI: How and Why It Occurs
How Bias in AI Originates
Bias in AI often originates from the data that is fed into machine learning models. AI systems are only as good as the data they are trained on, and if that data is biased, the AI system will reflect and potentially amplify that bias. Bias can be embedded in AI through several ways:
Training Datasets: AI models learn from data, but when that data is incomplete, skewed, or not representative of the real world, the model will generate biased predictions. For example, if an AI system for facial recognition is trained predominantly on lighter-skinned faces, it will struggle to recognize individuals with darker skin tones, leading to racial bias.
Algorithm Design: Bias can also occur at the algorithmic level. Algorithms may prioritize certain attributes over others, consciously or unconsciously reflecting the biases of the developers. For instance, an AI-driven hiring tool might favor candidates based on criteria that historically correlate with a specific gender or ethnicity, reinforcing discrimination.
Human Oversight: Human biases can unintentionally seep into AI systems during the design and development phases. Developers’ implicit biases, such as unconscious gender or racial preferences, can shape how they select data, design models, or choose evaluation metrics.
Technical Aspects of Bias in AI
The technical aspects of bias in AI involve complex interactions between data and algorithms. Some of the key challenges include:
Feedback Loops: AI models are often designed to learn and update based on new data continuously. However, if initial predictions are biased, those biases can become reinforced over time, creating a feedback loop of discrimination.
Underrepresentation: AI systems may be exposed to biased data because certain populations are underrepresented in the training dataset. For example, in medical research, women and minorities are often underrepresented, leading to AI models that may be less effective for these groups.
Skewed Labeling: Data labeling—where human workers tag datasets to train AI models—can introduce bias. If labelers’ biases influence how data is tagged (e.g., tagging images of certain professions as male-dominated), the AI model will reflect those societal biases.
Why Bias Continues to Persist in AI
Despite advancements in AI, bias remains a pervasive issue due to a combination of historical, cultural, and systemic factors. Together, these factors contribute to the persistence of bias in AI, underscoring the importance of diverse teams, unbiased data, and proactive efforts to address systemic inequities.
Historical Data: AI systems rely heavily on existing data, often reflecting past inequalities. For example, if a law enforcement AI is trained on data from historically over-policed communities, it may disproportionately flag individuals from those same communities as high-risk, perpetuating systemic biases. This reliance on biased historical data reinforces existing patterns of discrimination.
Cultural Influences: Cultural norms and prejudices significantly impact the data that AI systems ingest and how it’s applied. Gender stereotypes, racial biases, and economic disparities all influence the data used to train AI models, leading to outcomes that reflect these biases. This cultural bias becomes embedded in AI systems, creating a cycle of biased decision-making.
Lack of Diversity in AI Teams: The lack of diversity in AI development teams further exacerbates bias. Homogeneous teams may fail to recognize the biases present in their algorithms, resulting in systems that reflect the perspectives and experiences of the dominant group. Without diverse voices to identify and mitigate these biases, AI systems often reproduce the prejudices of those designing them, amplifying their real-world impact.
Consequences and Mitigation of Bias in AI
Consequences
Hiring and Employment: AI-driven hiring tools can discriminate against underrepresented groups by favoring candidates with characteristics traditionally linked to men or certain racial groups, leading to gender and racial bias and reinforcing workplace inequalities.
Credit Scoring and Financial Services: Bias in AI systems for credit scoring can negatively impact marginalized communities by lowering credit scores or denying loans to individuals based on historical patterns of exclusion, perpetuating poverty, and limiting economic mobility.
Law Enforcement and Criminal Justice: Predictive policing tools can perpetuate racial profiling by disproportionately targeting specific communities based on biased crime data, leading to over-policing, wrongful arrests, and mistrust in law enforcement.
Healthcare and Medical Diagnostics: AI algorithms in healthcare can lead to disparities in diagnosis and treatment, as models trained on biased data may underdiagnose conditions in women and minority groups, worsening health inequalities.
Education and Admissions: AI-driven school admissions and assessments can disadvantage students from certain socioeconomic or racial backgrounds, limiting their educational opportunities and reinforcing systemic inequalities.
Insurance and Risk Assessments: Bias in AI systems used for insurance underwriting may unfairly classify individuals from lower-income or minority groups as high-risk, resulting in higher premiums or coverage denials.
Public Services and Welfare: AI systems that determine welfare eligibility can misclassify individuals from disadvantaged communities, denying them access to essential services and deepening social inequalities.
The broader impact of these biases can lead to a loss of public trust in AI systems, eroding confidence in their fairness and reliability. Biased AI systems may also violate anti-discrimination laws or privacy regulations, posing a risk of legal consequences.
Strategies to Mitigate Bias
Despite these challenges, several strategies exist to mitigate bias in AI:
Diversifying Training Data: Ensuring diverse and representative data mitigates bias. Data should reflect various demographic, cultural, and socioeconomic backgrounds to avoid reinforcing historical biases.
Bias Audits and Monitoring: Regular bias audits help identify and address issues early. Continuous monitoring ensures AI systems remain fair as they are updated or exposed to new data.
Algorithmic Fairness: Fairness-aware machine learning models can reduce bias by prioritizing equity. For instance, in hiring, algorithms can be adjusted to limit gender or racial biases.
Explainable AI (XAI): Transparent AI models enable users and developers to understand decision-making processes, allowing for bias detection and correction while enhancing accountability.
Ethical AI Frameworks: Incorporating ethical guidelines ensures fairness, transparency, and accountability from the outset, promoting socially responsible AI development.
Inclusive AI Teams: Diverse teams help identify and mitigate biases during development. A range of perspectives can uncover blind spots often missed by homogeneous teams.
Bias Testing Metrics: Standardized evaluation metrics and tools can measure fairness and track progress, ensuring continuous improvements in reducing bias.
Collaboration with Experts: Partnering with ethicists and sociologists offers insights into AI’s societal impact and ensures systems adhere to ethical standards.
Government Regulations: Regulations like GDPR and the AI Act enforce fairness, transparency, and accountability in AI systems, pushing organizations to mitigate bias proactively.
Where Do We Draw the Line in Using AI?
AI has an incredible capacity to drive innovation, efficiency, and growth across industries, but this power also brings significant responsibility. The ethics in AI conversation is crucial because AI can have far-reaching effects on privacy, security, and individual autonomy. Deciding where to draw the line in using AI comes down to determining the ethical, moral, and societal limits that prevent harm.
For example, the use of AI in surveillance is highly debated. AI-enabled facial recognition systems are becoming more common in public spaces, used by both private companies and governments. While these systems can enhance security, they also pose serious privacy concerns. Is it acceptable for governments to track citizens’ movements without their consent? What are the risks of such technology being abused by authoritarian regimes, leading to mass surveillance and control?
Facial recognition also introduces bias. Many systems struggle to accurately identify people from certain demographic groups, particularly racial minorities. This has led to misidentification, wrongful arrests, and increased scrutiny on specific communities, raising the question: where do we draw the line between security and the potential for racial discrimination?
These dilemmas—choosing between efficiency and the risks of ethical compromises—are common challenges for organizations looking to adopt AI. The concerns are valid, but when AI is implemented cautiously, with the guidance of experienced vendors and industry-specific expertise, businesses can achieve greater efficiency while upholding ethical standards.
Balancing Innovation and Ethical Responsibilities
As AI continues to evolve, the challenge lies in balancing the immense potential of AI innovation with its ethical responsibilities. Developers must ensure that their systems not only meet technical standards but also align with societal values like privacy, fairness, and human rights.
For example, AI systems used in healthcare can assist in diagnosing diseases more accurately and efficiently. However, biases embedded in AI algorithms may lead to disparities in treatment for different racial, gender, or socioeconomic groups. Balancing the life-saving potential of AI with ensuring equitable access to care for all patients is a critical ethical consideration.
Another ethically contentious area is AI in hiring. Many companies have turned to AI-driven tools to screen resumes and identify the most suitable candidates. While this improves efficiency, the technology can perpetuate biases, as seen in some cases where AI algorithms favored male candidates over female ones based on biased historical data. Ensuring that AI doesn’t unfairly disadvantage certain groups requires constant vigilance, diversity in datasets, and the development of bias-free algorithms.
The Current State of Bias in AI
Bias in AI remains a significant and widespread issue across various sectors, from healthcare to law enforcement to finance. Despite advances in AI, recent research indicates that these systems continue to reflect and amplify the biases present in their training data. This is particularly troubling in critical areas where biased outcomes can have severe consequences for individuals and groups.
For instance, in healthcare, several studies have shown that AI systems used to predict medical conditions or prioritize care tend to underperform for minority groups. A well-known case involved an algorithm used in U.S. hospitals to determine which patients would receive extra medical attention. The system was found to be biased against Black patients, often underestimating the severity of their conditions compared to white patients with the same symptoms.
Similarly, AI in hiring processes has faced scrutiny due to its potential to perpetuate gender and racial biases. For example, a hiring algorithm used by a major tech company was found to be biased against women because it was trained on resumes submitted primarily by men over a decade. This bias affected the algorithm’s ability to evaluate female candidates fairly.
In law enforcement, predictive policing algorithms have drawn attention to disproportionately targeting minority communities. These systems often rely on historical crime data, which may reflect biased policing practices. As a result, the AI tools may direct more police resources toward communities that have been over-policed in the past, reinforcing cycles of discrimination.
Emerging Trends in Mitigating Bias
Despite these challenges, significant efforts are being made to reduce bias in AI. Both private companies and government organizations are increasingly focused on addressing fairness and accountability in AI systems. Some emerging trends include:
AI Fairness Tools: Several tech companies have developed fairness tools aimed at detecting and mitigating bias in AI systems. These tools can help developers identify biased data patterns and adjust models to ensure more equitable outcomes. For example, IBM’s AI Fairness 360 is an open-source toolkit designed to examine datasets for bias and provide recommendations for reducing it.
Algorithmic Transparency: There is a growing movement toward making AI models more transparent. Explainable AI (XAI) is an area of AI research that focuses on developing models that can explain their decision-making processes. This transparency allows developers to understand how and why an AI made a particular decision and helps identify areas where bias may have been introduced.
Active Bias Reduction Models: Some research groups are working on AI models that actively reduce bias in decision-making. These models are designed to adjust their predictions in real-time based on fairness metrics, helping to ensure more balanced and unbiased outcomes. However, while promising, these models are still in development and come with limitations.
Governments are also taking steps to regulate AI, with initiatives like the AI Act in the European Union leading the way in ensuring fairness and accountability. The act would impose stricter requirements on AI systems used in sensitive areas like hiring, healthcare, and law enforcement, ensuring that they meet ethical standards.
Conclusion
Addressing bias in AI is about more than just creating effective technology—it’s about ensuring that these systems are just, fair, and equitable. Developers must take into account ethical considerations in every phase of AI development, from data collection to deployment. Ethical frameworks, such as those advocating for fairness, accountability, and transparency, are increasingly being adopted by organizations and governments alike.
Collaboration between AI developers, ethicists, and policymakers is crucial for building ethical AI systems. These partnerships will help ensure that AI technologies align with societal values and work for the benefit of all, not just a privileged few.
Are you interested in learning more about how Conversational AI can benefit your business? Book a demo with one of our experts.
The field of AI is advancing rapidly, especially in large language models. Prominent models like GPT-3 and GPT-4 have impressive capabilities in generating coherent, human-like text. However, these models face a significant limitation: they rely solely on the data they were trained on, often leading to outdated or contextually incorrect information. As a result, the quest for more accurate, real-time, and contextually aware models has led to the emergence of Retrieval Augmented Generation (RAG).
RAG combines the generative power of large language models with the precision of information retrieval systems. By augmenting the generative model’s responses with real-time, relevant data fetched from external knowledge bases, RAG opens up new possibilities for generating accurate, up-to-date, and contextually rich information. In this article, we’ll dive into what RAG is, how it works, and why it is a game-changer in the world of AI.
What is Retrieval Augmented Generation (RAG)?
At its core, Retrieval Augmented Generation is a hybrid approach that fuses two powerful AI techniques: information retrieval and text generation.
Traditional language models, such as GPT-3, rely on vast amounts of pre-trained data. While these models are adept at producing fluent and coherent responses, they are limited by the static nature of their training data. As a result, they may produce factually incorrect or outdated information, often referred to as “hallucinations.”
RAG addresses this limitation by incorporating a retriever component that pulls in real-time, relevant information from external knowledge sources. The generative model then processes this information, producing responses that are linguistically accurate and grounded in factual, up-to-date content. In other words, RAG enhances the response generation process by accessing current data, reducing the likelihood of producing incorrect or irrelevant outputs.
The concept is simple: instead of solely relying on a model’s “memory,” RAG taps into a dynamic source of knowledge to improve the quality of its outputs. This combination of retrieving information and generating text leads to a far more robust, accurate, and context-aware language model.
Key Components of RAG
To understand how RAG achieves its enhanced capabilities, it’s important to break down its three core components:
Retriever
The retriever is responsible for fetching relevant content from external data sources. These sources could be anything from a curated knowledge base (like Wikipedia) to domain-specific repositories (like legal documents or scientific papers). The retriever scans the available information and identifies which passages or documents are most relevant to the query. This step ensures that the model can access up-to-date and contextually appropriate data.
Generative Model
The generative model works in conjunction with the retriever to synthesize responses. Unlike standalone generative models, which rely solely on pre-trained data, the generative component of RAG integrates the retrieved information into its output. This results in responses that are coherent and factually accurate, addressing one of the major challenges faced by traditional language models.
Knowledge Base
The quality and scope of the knowledge base are critical to RAG’s success. Whether it’s an internal database, a collection of documents, or an open-source platform like Wikipedia, the knowledge base serves as the retriever’s resource pool. The richer and more diverse the knowledge base, the better the retriever can perform in delivering accurate information to the generative model.
How Does Retrieval Augmented Generation Work?
RAG operates in two key phases: retrieval and generation.
Retrieval
The first step in RAG is retrieving relevant information. When a query is made, the system doesn’t immediately generate a response like a traditional language model would. Instead, it first identifies and pulls data from a vast pool of external sources, such as a knowledge base, a document repository, or even the web.
This retrieval process is powered by retriever models, typically trained using techniques like dense passage retrieval (DPR). These models learn to efficiently search through vast amounts of unstructured text to locate passages or documents that are most likely to contain relevant information. The key is that the retriever does not provide a final answer—it merely presents the most relevant chunks of data to the generative model.
Generation
Once the relevant information is retrieved, the generative model steps in. The role of the generative model, often a transformer-based architecture like GPT-3 or GPT-4, is to synthesize a coherent, natural language response. It does this by combining the retrieved information with its own pre-trained knowledge.
The generative model takes into account the context of the query and the retrieved data, integrating them to produce a well-rounded response. This fusion of information retrieval and generation ensures that the model’s output is fluent, accurate, and up-to-date information.
Together, these two steps create a system that produces responses with higher accuracy and relevance than purely generative models. RAG effectively bridges the gap between static knowledge inherent in traditional models and dynamic, real-time information retrieval systems.
What Are the Benefits of Using Retrieval Augmented Generation (RAG) Over Standard Generative Models?
RAG offers several distinct advantages over traditional generative models, making it a powerful tool for a variety of applications. Some key benefits include:
Better Responses with Increased Accuracy
One of the most significant advantages of RAG is its ability to produce more accurate responses. By retrieving relevant data from external sources, RAG reduces the likelihood of generating incorrect or outdated information. This makes it ideal for applications that require up-to-date and factual content, such as customer support, research, and legal analysis.
Reduced Hallucination
Traditional language models sometimes generate information that seems plausible but is entirely fabricated. This phenomenon, known as “hallucination,” can be problematic in critical applications like healthcare or finance. RAG mitigates this issue by grounding its responses in real data retrieved from reliable sources, resulting in more trustworthy outputs.
Context-Awareness
RAG’s retrieval mechanism allows it to provide more contextually relevant responses. Instead of generating generic answers based solely on pre-trained knowledge, the model tailors its output based on the specific information retrieved from external data sources. This leads to a more personalized and context-aware user experience.
Dynamic Knowledge Access
Unlike traditional models that require retraining to incorporate new data, RAG can access dynamic, real-time information without the need for extensive retraining. This flexibility allows it to adapt to new developments, such as changes in legal regulations, market trends, or scientific discoveries, making it more suitable for industries where information is constantly evolving.
Conclusion
Retrieval Augmented Generation represents a significant leap forward in the evolution of AI language models. By combining the strengths of information retrieval systems with the power of generative models, RAG produces responses that are not only coherent and contextually relevant but also grounded in accurate, up-to-date information. This hybrid approach has the potential to revolutionize a wide range of industries, from customer support and legal analysis to research and education.
As the availability of large, diverse datasets continues to grow and retrieval mechanisms improve, RAG will likely become an essential tool in the AI toolkit. Its ability to dynamically integrate new information, reduce hallucination, and provide context-aware responses makes it a promising solution for the next generation of AI-powered applications. The future of language models is not just about generating text—it’s about generating the right text, and RAG is leading the way in this exciting new frontier.
Are you ready to take the next step toward call automation with Conversational AI? Schedule a free demo with one of our experts to learn more!
Debt Collection and Positive CX: Is It an Oxymoron?
Customer experience and debt collection might seem like an oxymoron at first glance. After all, for most people, the thought of being reminded about an outstanding debt is far from enjoyable. The perception of collection calls as uncomfortable or even stressful is widespread. However, just because these calls aren’t the most welcome interactions doesn’t mean the customer experience (CX) has to be negative or impersonal.
At Skit.ai, we offer an effective and easy-to-deploy Conversational AI solution for debt collection use cases across multiple industries. There are many ways to make the interaction between a user and an AI solution efficient, easy to navigate, and painless. Enhancing the customer experience is particularly important when Voice AI is used in collection calls.
In this article, we’ll share the best practices for improving CX in automated collection calls, from multichannel communication to hyper-personalization and empathy.
How Does Omnichannel Communication Improve Customer Experience?
One key element that can significantly improve customer experience in debt collection is omnichannel communication. In an age where people are more active on digital communication platforms, consumers engage with brands and businesses through multiple channels, and debt collection should be no different.
By offering communication across various channels and platforms—such as voice calls, SMS, email, and chatbots—businesses give customers the flexibility to choose the method they feel most comfortable with. Omnichannel communication allows debt collectors to meet customers where they are, improving the likelihood of engagement and making the overall experience less invasive.
Imagine a scenario where a customer receives an SMS reminder about their debt and then follows up with an email. The customer might prefer to address the issue via email rather than a phone call, where they feel less pressured. By offering a variety of touchpoints, businesses can increase their chances of successful collections while also respecting the customer’s preferences.
Omnichannel communication also enhances customer experience by ensuring consistency across platforms. With AI-driven automation, every channel can carry the same messaging tone, verbiage, and information, ensuring the customer receives clear, concise, and friendly communication regardless of how they choose to engage.
Does Hyper-Personalization Help?
Yes, hyper-personalization does help, and it’s critical in improving customer experience in debt collection.
Generic, one-size-fits-all communication is not only impersonal but can also be perceived as insensitive, especially in a context where financial difficulties may be at play. Personalization goes a long way toward making customers feel respected and understood.
With AI-driven solutions, businesses can leverage data to hyper-personalize communication at scale. Instead of a standard message, imagine a conversation where the system addresses the customer by name, acknowledges their specific payment history, and offers tailored payment options that suit their financial situation. This type of personalization demonstrates a level of care and understanding that significantly softens the interaction.
Hyper-personalization also allows companies to provide a more humanized experience despite the conversation being led by AI. In debt collection, where emotions might run high, personalization can reduce friction and make the experience feel less transactional.
Can AI-Powered Collection Conversations Be Empathetic?
A key misconception about automated debt collection calls is that they can’t be empathetic. In reality, empathy is a cornerstone of positive customer experience, and it can absolutely be incorporated into AI-driven collection conversations.
Empathy in debt collection is not about avoiding the subject of payment—it’s about understanding the customer’s perspective and approaching the conversation with sensitivity. A well-designed AI solution can include language that acknowledges the customer’s situation and offers helpful solutions.
For instance, instead of a robotic, “You owe $X, please pay now,” an empathetic AI solution might say, “We understand that managing finances can be challenging. We’re here to help you resolve your outstanding balance in a way that works best for you.”
This shift in tone not only makes the conversation feel more supportive but also increases the likelihood of cooperation from the customer. When customers feel that the company understands their challenges, they are more open to resolving their debt.
3 Essential Tips to Ace Customer Experience in Collections
Here are three essential tips for improving customer experience in debt collection communications:
Use Conversational AI to Personalize at Scale
Personalizing each conversation is crucial in making the interaction feel human. AI can gather and analyze data to tailor responses based on each customer’s specific situation, allowing businesses to deliver personalized communication at scale.
For instance, instead of sending a generic reminder message, the AI can address the customer by name, reference their unique account details, and provide tailored options for resolving the outstanding debt. A message like, “Hi Sarah, we noticed that your last payment was on August 10th. Would you like to set up a payment plan to clear your remaining balance?” is much more engaging than a cold, “Your payment is overdue.” This simple gesture of personalization can dramatically improve the customer’s perception of the interaction and increase their willingness to cooperate.
In addition, AI can adjust its tone and language based on the customer’s previous interactions and responses. This adaptability ensures that customers feel understood and that the communication remains relevant and respectful, no matter where they are in their debt repayment journey. Personalized interactions also show the customer that their individual circumstances matter, which can help build trust and encourage more positive outcomes.
Incorporate Empathy in Your AI Conversations
Incorporating empathy into debt collection conversations is not just a nice-to-have; it’s a necessity for improving customer experience. Collections can be a stressful and emotional process for customers, and if the communication lacks empathy, it can feel cold, impersonal, and even confrontational. While many assume that AI can’t be empathetic, the truth is that empathy can be programmed into AI solutions, making the interactions feel more supportive and human-like.
Empathy in collections doesn’t mean avoiding the topic of debt—it’s about acknowledging the customer’s situation and offering constructive, respectful solutions. AI can be designed to recognize and respond to emotions, such as frustration, confusion, or anxiety, and modify its responses accordingly. For example, if a customer indicates they are struggling financially, the AI can respond with understanding and offer helpful alternatives, such as extended payment plans or reduced payment options.
For instance, instead of saying, “You are overdue on your payments,” an empathetic AI might say, “We understand that managing finances can be challenging. Let’s explore options that might help you with your current situation.” This shift in language not only makes the customer feel heard but also reduces the adversarial nature of the conversation.
Additionally, empathy can improve the likelihood of successful debt resolution. When customers feel that the company is genuinely trying to help them rather than simply collecting money, they are more likely to engage and cooperate. Empathy can turn a typically stressful interaction into an opportunity for the company to demonstrate care, which in turn, fosters customer loyalty and retention.
Offer Omnichannel Communication for Flexibility
Omnichannel communication is another essential strategy for improving customer experience in debt collection. Customers today expect the convenience of interacting with businesses on their terms across multiple platforms. By offering communication across various channels—such as voice calls, SMS, email, or chat—businesses can cater to individual preferences and make the collection process more comfortable and accessible for the customer.
For example, some customers may prefer the immediacy and directness of a phone call, while others might feel more comfortable responding to a less intrusive text message or email. Giving customers the choice of how to engage with the collection process enhances their sense of control and makes the interaction feel less invasive. The more flexible and convenient the communication options, the more likely customers are to respond positively.
Omnichannel communication also allows for a more seamless and consistent customer experience. Whether a customer interacts with a voicebot over the phone, sends a message via SMS, or replies to an email, the AI-driven system ensures that the same tone, information, and context are maintained across all channels. This consistency is key to building trust and ensuring that customers don’t feel like they are being bombarded with conflicting messages.
Omnichannel flexibility also provides a safety net for businesses. If one communication method is unsuccessful, the AI can follow up via another channel, increasing the chances of customer engagement. For instance, if a customer doesn’t respond to an email, the system can trigger an SMS reminder, ensuring the message gets across while maintaining a respectful distance.
Conclusion
Debt collection doesn’t have to come at the cost of customer experience. With the right tools and strategies, such as AI-driven automation, hyper-personalization, omnichannel communication, and empathetic language, businesses can turn even the most challenging conversations into opportunities to build trust and rapport with their customers.
At Skit.ai, we’re redefining the art of collection communication, ensuring that positive customer experiences remain a top priority, even during difficult conversations. Skit.ai is not just a leader in Conversational AI; we are innovators committed to empowering businesses with advanced AI technologies. By simplifying customer interactions with data-driven strategies and reaching users through their chosen communication channels, we help businesses achieve better collections and improve their operations. As we continue to evolve, we remain dedicated to driving success for our clients and setting new standards in the industry.
Are you ready to take the next step toward call automation with Conversational AI? Schedule a free demo with one of our experts to learn more!
The current economic volatility is affecting auto finance companies directly, and so are inflation and other consumer behavioral trends, making it a genuinely complex space to be in.
Tracking the Fitch Ratings on Subprime Auto ABS (Asset Backed Security) provides a better understanding of the circumstances in 2024:
Subprime auto delinquency rose to 6.39% in February, the highest in the decade.
The industry usually expects a recovery in March and April due to tax refunds. Delinquency rates did drop to 5.23% in April.
Even with this drop, the delinquency trends are the highest in the last decade. The recovery rates were the second worst, second only to the April 2020 rates.
With the auto finance industry expected to grow at a 7% CAGR, controlling delinquencies in a less affordable market troubled by high inflation rates is a challenge.
In addition to these macroeconomic stresses, auto finance companies face an acute skilled labor shortage. This cumulative effect has made auto finance companies scout for automation solutions that can solve their challenges on all fronts.
Relying solely on traditional collection strategies is not enough in this current landscape. Adopting innovative technological solutions has become a necessity. Before Conversational AI, no tech had the capability to automate collections and customer support calls without the need for agent intervention.
How Omnichannel Conversational AI Can Empower an Auto Finance Company with Automation
Omnichannel communication is a critical component of automating collections through AI. Omnichannel communication refers to using multiple channels, such as email, SMS, phone calls, and webchat, for collection agencies to engage with customers. By utilizing multiple channels, AI-driven systems can effectively reach customers, providing convenient avenues for them to address and resolve their delinquencies.
This approach caters to consumer preferences by offering a range of communication options, ensuring that each customer can engage using their preferred channel. Furthermore, this strategy is context-based, meaning they can seamlessly switch between channels without losing the context of their previous interactions.
Conversational AI for Inbound Communications
Inbound communications are a gold mine for collections. Customers often reach out with various inquiries, frequently seeking help to make a payment or process their transaction.
Without Conversational AI: Most auto finance collection processes depend on agents to handle inbound customer inquiries. However, if customers attempt to reach your business during off-hours or on weekends and holidays, they often can’t get connected with an agent, leading to missed payment opportunities. Moreover, maintaining a well-trained team of agents amid rising attrition makes it increasingly challenging to provide reliable inbound support, making it harder for customers to simply make their payments.
With Conversational AI: All inbound communications are seamlessly managed. Every call is answered, every SMS and email receives a reply, and no payment opportunity is missed. It can also schedule follow-ups and calls for later if requested or needed.
Conversational AI’s Impact:
Better collections as willing consumers have access to easy payment options
Better disposition and intent capture
Improvements in CSAT scores
Better customer experience as financiers can listen to and record every customer query.
Conversational AI for Outbound Outreach
Conversational AI revolutionizes customer outreach by automating end-to-end collections, allowing auto finance companies to efficiently scale their operations.
Without Conversational AI: Traditionally, reaching out to customers involved significant manual effort, with agents making individual calls and sending messages.
With Conversational AI: Collections are streamlined, enabling thousands of calls to be placed and SMS or emails to be sent within minutes. This not only saves time but also ensures that outreach efforts are consistent, widespread, and personalized, significantly improving scalability and operational efficiency.
Automated Outreach for Better Auto Finance Collections
Omnichannel Outreach for Maximum Engagement
Today’s customers—especially those from Gen Z—prefer to interact across various communication channels. They are no longer limited to a single mode of communication like phone calls; instead, they expect to be reached via SMS, email, or other digital platforms. Conversational AI excels in this area by facilitating omnichannel outreach, ensuring communication is tailored to each customer’s preferred method. By engaging customers where they are most active, auto finance companies can achieve higher response rates and better engagement.
Seamlessly Consistent Communication
A key advantage of using Conversational AI for outreach is its ability to integrate and never lose the context of communications across all channels. Whether a customer responds via email, SMS, or phone, the AI ensures that all interactions are a part of a unified communication strategy. This prevents any communication gaps or inconsistencies, providing customers with a smooth and coherent experience.
End-to-End Collections without Agent Intervention
Conversational AI can establish right-party contact, capture promise-to-pay (PTP), and facilitate on-call payment collection without needing any agent intervention until asked for or during any complex situation. Customers can easily make payments using a card-on-file or through a secure text-based payment link, streamlining the payment process. This approach can also lead to faster collections without compromising the customer experience, ensuring that payments are collected promptly while maintaining a positive relationship with the customer.
Facilitating Negotiations and Payment Plans
Conversational AI is not just about collecting payments—it can also handle more complex interactions, such as negotiating payment terms and setting up customized payment plans. For customers facing financial difficulties, the AI can offer flexible solutions that align with their current situation, such as extended payment deadlines or installment plans.
Automated Payment Reminders
Conversational AI can automatically send payment reminders via SMS or other channels to further enhance the payment collection process. These reminders can be scheduled at optimal times to ensure they are received when the customer is most likely to take action. By proactively reminding customers of upcoming or overdue payments, the AI reduces the likelihood of missed payments and improves overall collection rates. This feature is especially useful in maintaining regular cash flow and ensuring customers remain on track with payment obligations.
Impact of Conversational AI
Conclusion
Omnichannel Conversational AI offers auto finance companies a powerful tool to automate and optimize their collections process. By integrating multiple communication channels and leveraging AI-driven technology, companies can enhance customer engagement, streamline operations, and achieve faster debt recovery without sacrificing customer experience.
Whether it’s managing inbound communications, automating outreach, facilitating negotiations, or sending timely payment reminders, conversational AI provides a comprehensive solution that drives better outcomes. As the auto finance industry evolves, embracing this technology will be key to staying competitive, improving profitability, and building stronger, more responsive customer relationships.
Are you interested in learning more about how Conversational AI can improve your collections strategy? Book a demo to schedule an appointment with one of our experts!
The auto finance industry is experiencing significant transformations driven by market dynamics, consumer behavior, and technological innovations. Here are the key trends shaping the future of auto finance, focusing on the implications for Buy Here Pay Here (BHPH) dealers and the role of Conversational AI and contact center automation in streamlining operations, which will help the industry navigate turbulent times.
Key Trends
Increased Vigilance Required for BHPH Players
The demand for used cars has surged, putting pressure on BHPH players to be more cautious and vigilant about their loan approvals and collection processes. With the rise in used car sales, BHPH dealers must maintain stringent oversight to mitigate risks associated with subprime auto loans. Effective loan management and collection strategies are crucial in ensuring financial stability and minimizing delinquencies.
Negative Equity and Rising Debt
Negative equity on car loans is emerging as a major concern. As car prices stabilize, many buyers are left with higher-than-average debt, resulting in them being underwater on their loans.
Rising Used Vehicle Loan Rates
Used vehicle loan rates have increased, averaging a 23 basis point (bps) rise year over year. This could potentially lead to higher delinquency rates and higher repossessions.
Longer Loan Terms at Record Levels
Both 60-month and 48-month auto loans are at their highest levels in the last 15 years. This shift towards longer loan terms makes monthly payments more affordable and may extend the repayment period. Without an efficient collection strategy, it may become difficult for auto finance companies to recover the loans.
Near Record-High Amounts Financed
The average amount financed for auto loans is nearing an all-time high of around $40,000 USD, reflecting the rising costs of vehicles.
Affordable New Car Rates and Transaction Trends
According to Moody’s Affordability Index, while the average transaction price for new cars has declined in 31 months due to more affordable rates in 2024, it remains one of the highest in a decade. This indicates a shifting market where affordability is improving, but high transaction values persist.
The Solution to Overcome the Current Environment: Contact Center Automation with Conversational AI
As the auto finance industry faces various challenges—from rising loan rates to increased negative equity—innovative solutions for a compelling collection are more critical than ever. Contact center automation with Conversational AI has emerged as a powerful tool for auto finance and BHPH companies.
Inbound Contact Center Automation: Enhancing Consumer Experiences
Zero Wait Time: Traditional Contact centers often frustrate consumers with lengthy IVR menus and extended wait times, leading to high drop-off rates. With an average of 15% to 20% of consumers dropping off at the IVR menu, there is a significant loss of collection opportunities. Implementing conversational AI systems like Skit.ai, which provide contact center automation, can eliminate wait times and enhance consumer satisfaction by providing immediate assistance.
Personalized Consumer Interaction: Conversational AI integrates with existing CRM systems to offer a personalized approach to consumer service. This integration allows the AI to recognize the consumer’s identity and recall previous interactions, providing a seamless and customized experience. Such systems can fetch consumer profiles in milliseconds, improving the efficiency and effectiveness of the service.
Best Engagement Channels: Today’s consumers are less likely to answer calls from unknown numbers, with over 90% ignoring such calls. Engaging consumers through SMS and voice can lead to higher response rates, as text messages have double the response rate of voice calls. Offering payment channels through both mediums can improve engagement and collection rates.
24/7 Inbound Support: The lack of support over weekends often leads to missed collection opportunities. By providing 24/7 consumer support, auto finance companies can ensure continuous engagement and reduce the chances of delinquencies.
Outbound Contact Center Automation: Maximizing Engagement and Recovery
Increased Attempts and Engagement: Higher engagement is essential for ensuring timely payments. Infinite scalability in outbound contact center automation allows for more attempts to contact consumers, which is crucial for BHPH players who cannot afford prolonged delinquent cycles. Increased engagement during the DPD 0-21 phase can significantly enhance recovery rates.
Prioritizing Loan Payments: Engaging consumers over weekends can prevent auto payments from being deprioritized. Most consumers get paid on Fridays, and without engagement, they may spend on non-discretionary items. Automated calls over the weekend can remind consumers of their auto payments, reducing Monday delinquencies.
Multichannel Payment Integration: Offering multiple payment channels and automating collections through phone payments can streamline the process. Integrating card-on-file or user-defined card options and setting up auto payments can improve collection efficiency.
Payment Negotiations and Alternative Plans: Consumers facing unforeseen events such as job loss or medical expenses need proactive engagement. Offering alternative payment plans based on their payment history can enhance consumer satisfaction and ensure better recovery rates.
Benefits of Using Skit.ai for Contact Center Automation
Experience and Trusted Name: Skit.ai is a trusted name in the auto finance industry, featured among the top 500 companies in Auto Remarketing. It collaborates with renowned names such as Veros Credit, PeakBHPH, and Sensible Auto, ensuring credibility and reliability.
Low Lift Integration Effort: Skit.ai offers seamless integration with built-in dialer platforms and CRMs like DealerSocket- IDMS and Automaster. It also integrates with common payment gateways such as PayNearMe, making the transition to automated systems smooth and efficient.
Conclusion
Integrating Conversational AI and contact center automation is not just a technological upgrade but a strategic shift toward a more efficient, consumer-centric, and financially robust collection model. Companies that embrace these technologies will be better positioned to navigate the complexities of the modern auto finance landscape, stay ahead of the competition, and deliver superior experiences to their consumers and stakeholders.
As the auto finance industry evolves, adopting conversational AI and contact center automation will be key to enhancing operations, providing a better consumer experience, and improving recoveries with minimal effort.
Curious to learn more about how Skit.ai’s Conversational AI can maximize your account penetration? Book a free demo with one of our experts.
The auto finance industry, a crucial pillar in the automotive market, experienced a turbulent Q2 in 2024. The rise of delinquent accounts in subprime lending has become a significant concern for industry stakeholders. Subprime lending, which targets borrowers with lower credit scores, is inherently riskier, and recent economic pressures have worsened these risks. This blog delves into the current landscape of the auto-finance industry, especially last quarter Q2, and discusses how the industry can tackle this concern.
Subprime Lending in the Auto-Finance Industry
Subprime lending involves offering loans to borrowers with lower credit scores, typically below 620. These borrowers are considered higher risk due to their credit history, including previous delinquencies, defaults, or bankruptcies. Lenders often charge higher interest rates and fees to compensate for the higher risk. In the auto-finance industry, subprime loans enable a broader demographic to purchase vehicles. However, this lending segment is also more vulnerable to economic fluctuations.
The Current Landscape: Delinquent Accounts on the Rise
In 2023, the auto loan delinquency ratio at U.S. banks reached its highest level in the past decade. According to S&P Global Market Intelligence data, the delinquency ratio at U.S. banks was 3.32% at the end of 2023, the highest since 2013. This increase occurred even though the industry’s total amount of auto loans fell to $530.38 billion from $548.40 billion in 2022, marking the first year-over-year decline since 2013.
Fitch Ratings says delinquent accounts and net losses have been trending higher while recovery rates have fallen, signaling weakened performance across the board in Q2 of 2024. Historically, the first quarter of the year benefits from a seasonal boost as borrowers utilize tax refunds to catch up on delinquent loans. However, in 2024, this boost was notably weaker. Economic pressures, coupled with greater outstanding balances from weaker-performing assets, have diminished the positive impact typically seen from January to April.
In April 2024, the delinquent account rate stood at 5.23%, a decline from the all-time high of 6.39% recorded in February. This decrease follows the typical pattern where borrowers use their tax refunds to catch up on loan payments. However, the seasonal improvement this year was less pronounced than in previous years, with delinquent account rates at 4.67% in April 2023 and 3.86% in April 2022.
Recovery rates also suffered in April 2024, dropping to a low of 43.03%, a stark contrast to 54.96% in April 2023 and 62.51% in April 2022. This decline in recovery rates highlights the challenges lenders face in recouping funds from delinquent accounts.
Additionally, the net loss rate in April 2024 was 7.90%, significantly higher than the 6.16% observed in April 2023 and the 4.13% in April 2022. This increase in net losses underscores the financial strain on lenders within the subprime auto loan market.
What Can the Industry Do to Reduce Delinquent Accounts?
While the auto-finance industry cannot directly eliminate the rise in delinquent accounts among subprime borrowers, it can take steps to improve recovery rates. This cannot be done by simply increasing the number of collection agents. Although adding more agents might boost recovery rates to some degree, it would also significantly raise operational costs, which is not the way any company would want to go.
So, Is There a Solution?
The answer is yes.
Technology, particularly Conversational AI, has been a game changer for the auto finance industry. With the rising delinquencies, leveraging Conversational AI has become essential for auto finance companies to enhance their collection efforts and automate processes.
But how does Conversational AI help?
Conversational AI and automation technology can significantly enhance collection processes. By automating end-to-end collections, engaging borrowers, and ensuring compliance with regulatory requirements, these technologies contribute to higher recovery rates. A multichannel conversational AI platform can call and text customers any day of the week, engaging them in human-like conversations while maintaining compliance. It can handle the entire collections process, including customer verification, disposition capture, and payment processing, without needing agent intervention.
Conversational AI can dial thousands of calls per minute and send thousands of SMS, ensuring scalable, comprehensive, and compliant engagement across your consumer portfolio. Conversational AI can handle inbound queries and collect payments at any time, enabling 24/7 collections without requiring agent intervention. Additionally, AI-driven analytics provide valuable insights into borrower behavior, allowing lenders to customize their strategies and enhance overall collection efficiency.
Skit.ai’s Multichannel Conversational AI
Conclusion
The second quarter of 2024 has been a turbulent period for the auto-finance industry, marked by a rise in delinquent accounts within subprime lending. While economic pressures and weaker-performing assets have aggravated the situation, the industry’s response to adopting conversational AI to help improve collection efforts offers a path to stabilization. As we move into the year’s second half, all eyes will be on how these measures impact the broader landscape of subprime auto lending.
Curious to learn more about how Skit.ai’s Conversational AI can maximize your account penetration? Book a free demo with one of our experts.
Since the advent of ChatGPT, Conversational AI has received a significant boost across various industries. Conversational AI is no longer just automating minor tasks; it can now solve complex issues and provide meaningful resolutions to customers. This transformative technology enhances customer interactions by understanding context, emotions, and intent, leading to more personalized and effective communication.
Conversational AI has found applications across various industries, enhancing customer engagement, operational efficiency, and service delivery. In healthcare, Conversational AI facilitates patient interactions through virtual assistants that offer personalized medical advice and manage appointment scheduling. In finance, Conversational AI powers virtual financial advisors, providing real-time investment insights and transaction support. Retail utilizes chatbots for personalized shopping experiences, product recommendations, and customer support. In education, Conversational AI supports virtual tutoring and adapts learning materials to individual student needs.
Many Conversational AI companies in the US are significantly revolutionizing contact center operations. These companies offer solutions that are improving efficiency, ensuring better compliance, and enhancing customer satisfaction by leveraging advanced AI technologies. Let’s take a closer look at the top six conversational AI companies leading the way in the US.
Here are the top 6 conversational AI companies in the United States:
Amazon Lex
Freshworks
Sprinklr
Skit.ai
Yellow.ai
Kore.ai
Amazon Lex
Amazon Lex is a fully managed AI service equipped with advanced natural language models to design, build, test, and deploy conversational AI interfaces within any application using voice and text. Amazon Lex also powers the Amazon Alexa virtual assistant. Released to the developer community in April 2017, Amazon Lex can be used for a variety of conversational AI interfaces, including chatbots for web and mobile apps, as well as interactions for robots, toys, drones, and more. While Amazon Alexa Voice Services allows developers to integrate Alexa into their devices, Amazon Lex provides flexibility for end users to interact with any type of assistant or interface, not just Alexa. As of February 2018, users can define responses for Amazon Lex chatbots directly from the AWS management console.
Freshworks
Freshworks Inc., founded in 2010 in Chennai, India, is a cloud-based software-as-a-service company. It offers cloud-based tools for customer relationship management (CRM), IT service management (ITSM), and e-commerce marketing. One of its key products, the Customer Service Suite, is an all-in-one conversational AI customer support solution that enhances business-customer interactions. The suite enables personalized self-service experiences with conversational AI-powered chatbots, helping businesses optimize operational efficiency and deliver exceptional customer support. The Customer Service Suite equips businesses to anticipate customer needs and deliver unparalleled service experiences by providing a comprehensive view of customer conversations and integrating various tools using conversational AI.
Sprinklr
Sprinklr is an American software company based in New York City that develops a SaaS customer experience management (CXM) platform. The Sprinklr platform integrates various applications for social media marketing, social advertising, content management, collaboration, employee advocacy, customer care, social media research, and social media monitoring.
Sprinklr has integrated AI across four product suites: Sprinklr Service, Sprinklr Social, Sprinklr Marketing, and Sprinklr Insights, along with self-serve offerings. This unified platform, built on a single codebase with an operating system approach, provides customers with the tools they need to deliver exceptional experiences. By enabling seamless collaboration among customer-facing teams, markets, and geographies, Sprinklr offers brands a unified digital edge.
Skit.ai
Skit.ai is the leading Conversational AI company in the accounts receivables industry, enabling collection agencies and creditors to automate collection conversations and accelerate revenue recovery. Skit.ai’s suite of multichannel solutions—featuring voice, text, email, and chat in both English and Spanish, powered by Generative AI—interacts with consumers via their preferred channel, elevating consumer experiences and consequently boosting recoveries. Skit.ai has automated collection calls for many collection agencies in the US and several major banks in India.
Skit.ai is revolutionizing the accounts receivables industry by enabling companies to automate and accelerate consumer interactions at scale using Conversational AI. By integrating existing dialer systems, seamless conversational capabilities powered by Generative AI, and fast campaign analytics, Skit.ai’s suite of multichannel Conversational AI solutions retains context across channels, boosting efficiency and elevating consumer experiences.
Skit.ai has received several awards and recognitions, including the BIG AI Excellence Award 2024, Stevie Gold Winner 2023 for Most Innovative Company by The International Business Awards, and Disruptive Technology of the Year 2022 by CCW. Skit.ai is headquartered in New York City, NY.
Yellow.ai
Yellow.ai, formerly known as Yellow Messenger, is a multinational company headquartered in San Mateo, California, specializing in customer service automation using Conversational AI. Founded in 2016, the company provides an AI platform designed to automate customer support experiences across chat and voice channels. Supporting more than 135 languages and over 35 channels, Yellow.ai has become a global leader in generative AI-powered enterprise customer service automation. Yellow.ai’s platform helps enterprises achieve exceptional efficiency in customer service while significantly reducing operational costs. Their solutions cater to customer support, employee experience, and sales across BFSI, retail, and healthcare industries. The platform features an enterprise-grade conversational AI system with a no-code builder, making it accessible and adaptable for various business needs.
Kore.ai
Kore.ai develops an enterprise conversational AI and generative AI platform designed to help organizations design, develop, test, and manage chatbots for both internal and customer-facing scenarios. The company’s innovative platform, no-code tools, and solutions deliver comprehensive customer and employee experiences, from automated to human-assisted interactions, and support the creation of generative AI-enabled applications. Kore.ai adopts an open approach, allowing companies to select the LLMs and infrastructure that best suit their business needs and assists customers in navigating their AI strategy. With a strong patent portfolio and recognition as a leader and innovator by top analysts, Kore.ai is headquartered in Orlando and supported by a global network of offices.
DISCLAIMER This list is based on subjective research and experiences, along with information gathered from various online sources, including web articles and search engine results; it is not intended to imply any specific ranking order and should be used solely as a reference guide.
Curious to learn more about how Skit.ai’s Conversational AI can automate your contact center operations? Book a free demo with one of our experts.
High Deductible Healthcare Plans (HDHPs) have become the preferred insurance option for many Americans primarily due to their lower premiums. They are also a popular health insurance plan offered by private-sector employers. In 2022, more than half of U.S. private-sector workers (53.6%) were enrolled in HDHPs.
However, HDHPs have a higher deductible than traditional insurance plans, meaning individuals must cover more healthcare expenses out of pocket before the insurance company starts contributing.
In this blog post, we will discuss the rising popularity of HDHPs and their implications for RCM providers and Extended Business Offices (EBOs). Additionally, we will explain why Conversational AI technology is a game-changer for early-out collections.
Why Are High Deductible Healthcare Plans (HDHPs) Becoming Popular?
Rising Health Insurance Costs
With rising health insurance costs and hospital charges, people are opting for HDHPs, which have lower premiums. The American Medical Association (AMA) reports that healthcare costs are climbing at approximately 4.5% annually. In 2019, healthcare spending in the United States increased by 4.6%, reaching a staggering $3.8 trillion nationwide, equating to an average of $11,582 per person. This increase aligns closely with the rates seen in 2018 (4.7%) and slightly surpasses those of 2017 (4.3%).
Besides the ongoing trend of healthcare costs inching upward, short-term factors have also played a significant role. Many U.S. residents experienced faster-than-average increases in their health insurance costs in 2021, as insurance companies and healthcare providers raised costs post-pandemic.
As a result, over half of all U.S. workers were enrolled in high-deductible health plans (55.7%). Enrollment for HDHPs has risen for the eighth consecutive year in 2023, the highest enrollment rate since 2012.
HDHPs are Cheaper for Employers
Employers regularly seek strategies to offer stable benefits while reducing costs. This practice enhances their competitiveness in the job market while keeping expenses in check.
According to a recent Mercer study, larger employers spend an average of $84 per month on High Deductible Healthcare Plans (HDHPs) per employee, compared to $132 per month for traditional Preferred Provider Organization (PPO) plans. This shift towards HDHPs translates to a significant 37% reduction in costs per employee, with greater savings realized by larger companies.
Flexible Coverages
Beyond cost savings, HDHPs offer enhanced flexibility in healthcare coverage. Unlike Health Maintenance Organizations (HMOs), HDHPs typically impose fewer restrictions, granting individuals greater freedom to select their preferred service providers. This increased flexibility removes hurdles from the healthcare decision-making process, empowering individuals to make more informed choices about their healthcare options.
HDHPs = Savings
HDHPs can also provide additional savings opportunities for individuals. HDHP is the sole Health Savings Account (HSA)-eligible health plan that helps with additional savings. With an HDHP, individuals can establish an HSA to benefit from tax-free saving, investing, and spending on healthcare expenses. HSAs offer several advantages, including the ability to carry over funds yearly without expiration. Unlike other types of accounts, HSAs are owned by the individual and can be transferred between jobs and healthcare providers. Furthermore, HSAs serve as an additional retirement account.
This ownership of HSA funds provides stability amidst the ever-changing healthcare landscape, allowing individuals to retain their accounts even as they transition to different health plans each year.
What Does This Mean For RCM Providers and EBOs?
The increase in demand and adoption of HDHPs has significant implications for Revenue Cycle Management (RCM) providers and External Business Offices (EBOs) especially when collecting self-pay dues in early-out collections.
Let’s explore how this trend affects early-out collections and alters the revenue cycle for these businesses.
Increased Patient Self-Pay Dues = Delayed Cash Flow
HDHPs typically come with higher deductibles, meaning patients are responsible for a larger portion of their healthcare expenses upfront before insurance coverage kicks in. As a result, patients may delay or struggle to pay their medical bills, leading to a higher volume of outstanding balances in early-out collections.
With this delay, the revenue cycle for RCM providers and EBOs may lengthen as they wait longer to receive patient payments. This impact on the cash inflow can strain liquidity and hinder financial planning efforts.
Challenges in Collecting Payments
RCM providers and EBOs may encounter challenges collecting payments from patients with HDHPs. The increased self-pay responsibilities require increased engagement efforts from RCMs/EBOs to reach patients and collect payments. There is also a higher denial rate for self-pay dues. This requires additional resources, such as spending time resolving billing disputes and answering queries.
Need to Enhance Patient Communication
RCM providers and EBOs must prioritize communication to ensure patients are aware of their self-pay dues. This may involve explaining insurance coverage, clarifying billing statements, and offering payment plan options to facilitate timely collections.
Focus on Proactive Payment Strategies
RCM providers and EBOs must adopt proactive payment strategies to streamline billing and payment processes in response to the challenges posed by HDHPs. This approach facilitates the retrieval of self-pay obligations and fosters trust among patients.
How Does Conversational AI Help Expedite Early-Out Collections?
Skit.ai’s AI bot can initiate outreach to patients and engage with them in the following days via multiple channels, such as phone calls (Voice AI), text messages, emails, and chatbots, ensuring effective communication and engagement from the outset. It can manage complex, multi-turn conversations with patients across all channels, maintaining context seamlessly. The Voice AI solution engages with patients in human-like conversations. This ensures meaningful engagement with them and, at the same time, offers scalability to RCM providers to reach out to numerous patients in bulk, thus helping mitigate potential payment delays and improving patient satisfaction.
More Than Just a Call; Available at Patient’s Beck and Call
Skit.ai’s AI bot can authenticate patients, clarify bill breakdowns, answer patient queries, facilitate on-call payments and text-based payment links, and even set up payment plans, enhancing convenience and reducing barriers to receiving payment.
Enhanced Cash Flow
With more outreach, faster query resolution, and seamless payment options (on-call payments and text-based payment links), Skit.ai enables RCM and EBOs to do more early-out collections of self-pay dues.
Improved Efficiency and Reduced Agent Costs
Skit.ai’s AI bot augments human efforts by automating repetitive and time-consuming tasks, enabling RCM staff to focus on resolving complex disputes and providing personalized patient assistance.
Through automation, operational expenses are reduced, revenues are maximized, and overall productivity within the organization is enhanced. Additionally, this results in decreased staffing needs and reduced training costs for RCM providers and EBOs.
Conclusion
The surge in high-deductible healthcare plans (HDHPs) underscores a shift in early-out collections. The rise in patient self-pay dues under HDHPs requires increased engagement efforts and proactive payment strategies to streamline billing processes and enhance revenue cycles. Additionally, adopting Conversational AI solutions offers a promising avenue for overcoming these challenges.
Conversational AI improves efficiency, enhances cash flow, and reduces costs for RCM providers and EBOs by facilitating bulk outreach, providing comprehensive patient assistance, and automating repetitive tasks.
Curious to learn more about how Conversational AI can help you gain a competitive edge over your competitors? Book a free demo with one of our experts.
Abhinav Tushar, Skit.ai’s Head of Machine Learning, discusses how LLMs are reshaping automated consumer conversations in the collections space and their direct influence on enhancing the overall consumer experience.
What advancements in Conversational AI are you most excited about currently?
At Skit.ai, we focus on helping businesses derive value from Conversational AI. We’re particularly interested in enhancing LLM capabilities to achieve difficult conversational goals. While LLMs are capable of having high-quality conversations, they still struggle with reliability in multi-turn conversations. We are focused on aligning with the goals of both the users and the businesses we serve.
How have LLMs changed the way we think about Conversational AI?
The current generation of LLMs has solved the problem of handling believable and natural conversations. Despite some factual issues and minor glitches, LLM bots can maintain the flow of a conversation. Apart from this, there are exciting upgrades for spoken conversations, such as the improved ability to model any behavior that can be meaningfully translated into text. This progress aligns with the promises of Artificial General Intelligence (AGI), and it’s exciting to see us move in that direction.
These advancements are prompting a reevaluation of the potential of automation. For a product like ours—goal-oriented bots—we expect a reduction in modeling complexity to increase the extent of automation, even for dialogs that used to be considered the forte of live agents.
How do you envision the future of Conversational AI over the next few years?
Over the next few years, we’ll see a focus on extracting value from this technology. While chat and voice bots have been around for quite some time already, the emergence of LLMs has marked the beginning of a brand new chapter, in which we will see more experimentation with conversational modality added as part of many interfaces. At a lower level, we expect multimodal models to dominate, along with a lot of effort going into integrating these virtual assistants with diverse data sources enabling us to further personalize the interactions with users.
What are some best practices that you follow while working with AI systems?
The most important thing to do is establish clear, measurable, goal-oriented metrics. Safety metrics, like the conversational compliance rate, are crucial in our domain. Without an effective system to monitor business value, we risk deploying products that are either harmful or not useful. This requires a thorough understanding of the Machine Learning (ML) model lifecycle, which remains unchanged despite advancements in LLMs, even though the other intermediate tools have evolved.
What sets Skit.ai’s approach to Conversational AI apart from others in the industry?
Our Conversational AI stack is powered by LLMs connected with speech systems in a complete duplex manner to achieve naturalness, which is an industry standard. However, here are a few elements that set us apart from other providers.
Firstly, at Skit.ai, we prioritize compliance and data security. We use guardrails, flow guarantees, red teaming, reinforcement learning, and other techniques to ensure compliance checks are considered at every stage of model development, deployment, and runtime.
Secondly, for us, goal completion often involves multiple conversations with a user, possibly across multiple channels.
Lastly, we support multi-modality and believe in speech-first Conversational AI. Our approach aims to acknowledge and leverage non-vocal cues from conversations, which have historically been overlooked in real-time Conversational AI.
What are some challenges that companies face in extracting value from AI?
The two most common challenges in my experience have been (a) not thoroughly understanding how business metrics are connected with low-level models and (b) not respecting the model lifecycle once in production. With the rise of LLMs, executives in every company are pressured to incorporate them in some way, often leading to ineffective efforts. What’s needed is a two-way conversation between understanding your product’s value chain and an LLM’s capabilities. Additionally, as this technology evolves rapidly, it’s crucial to have a clear vision of the future to avoid working on problems that may soon become irrelevant.
How do you handle AI’s shortcomings in terms of fairness and bias?
Most of the shortcomings can be handled with a little extra effort. The type of models used, and the nature of the product being developed carry more significant bias implications than the algorithm itself. We ensure that our product’s usage complies with AI ethics regulations and regional deployment guidelines. At a lower level, we monitor fairness metrics, prevent the misuse of protected attributes by any model, and select fair algorithms wherever we need them. This is a challenging objective, and new learnings often emerge as we go. We strive to lead the way as we address bias.
Can AI in collections enhance compliance with regulations? If so, how?
Yes, absolutely. While ongoing efforts are essential to ensure compliance with existing and upcoming AI regulations in the collections space, we’re confident that overall compliance regulations are on the rise and will continue to improve with AI adoption. This is not surprising. In fact, there are solutions built specifically to handle and monitor human compliance. Humans make mistakes naturally, and that’s one of the reasons why automation scales. At Skit.ai, we enhance collections with AI not only by adding and improving communication channels but also by interconnecting them and learning from data to create a superior, error-free engine.
Curious to learn more about how LLMs can enhance your collections strategy? Book a free demo with one of our experts.