Today's credit landscape is in the midst of a revolution. Today’s borrowers — no matter what type of credit they’re seeking — want a straightforward, fast, and personalized lending experience. Lenders desire the same state-of-the-art customer experience that reduces drop-offs during the credit application process, improves customer satisfaction, and boosts revenue. However, legacy systems and infrastructure often hinder a lender’s ability to provide a seamless digital borrowing experience. With the advent of artificial intelligence (AI) and real-time data analytics, credit decisioning is becoming increasingly automated. AI in conjunction with real-time data analytics is being used to assess credit risk and make credit decisions more quickly and accurately than humans ever could.
This article will explore the following topics:
- Challenges with conventional credit decisioning
- The needs of modern credit decisioning
- The benefits of AI risk decisioning for lenders and borrowers
- Why AI risk decisioning is the future of credit decisioning.
Challenges with conventional credit decisioning
Traditionally, credit decisioning has been a manual process, requiring that borrowers wait for several days, sometimes weeks, to receive credit. Despite having a digital application process, such lending journeys are fraught with manual processes that rely on legacy software and fragmented approaches to data aggregation at the point of credit decisioning. Being human-intensive, such models cost companies a fortune in the process of scaling up and often fail to capture applicants without a verifiable credit history. Beyond this, long waiting periods also result in higher dropout rates, low satisfaction, and reduced business value for the stakeholders.
Conventional credit decisioning is detrimental to the future of lenders
Conventional credit decisioning can stunt the growth of lenders in several ways:
- Inability to meet borrower expectations: Manual credit decisioning processes fail to meet the modern borrower's expectations of a seamless and instant digital borrowing experience. Today, borrowers expect to receive a decision within minutes of applying for credit.
- Uncaptured business value: Many consumers have no credit history and may not qualify for traditional financing. Lacking the ability to aggregate multiple alternative data sources—such as income to debt ratio and history of paying back credit—conventional credit decisioning methods ignore many prospective points of sale and user personas.
- A digital illusion: While the application process might itself be digital, back-office decisioning processes are still manual. Therefore, conventional credit decisioning not only comes at a higher cost but also introduces a bottleneck in the rate of growth and the possibility of upscaling.
- Lack of transparency: Manual credit decisioning is error-prone and susceptible to prejudice, which makes it difficult for the organization as a whole to get visibility into its overall risk exposure.
The needs of modern credit decisioning
Modern credit decisioning has several capabilities that are absent in conventional credit decisioning tools:
- Combining traditional credit data with alternative data sources—such as telecom, utilities and specialty finance—can help drive more informed credit risk decisions.
- Moving beyond traditional modeling techniques, such as logistic regression, to calculate credit worthiness.
- Applying advanced analytics and machine learning to reveal patterns of financial behavior that can help predict a consumer’s financial trajectory
- The predictive nature of trended credit data can help strengthen analytics and credit model development.
Addressing the above needs of modern credit decisioning is not merely an incremental change for conventional credit decisioning systems; it requires building an entirely new foundation. This foundation has fundamental characteristics that lends itself to addressing the core requirements of a modern credit decisioning system. And that new foundation is built on AI and data analytics.
AI risk decisioning is the future of credit underwriting
AI risk decisioning is the ability to use real-time data analytics and Artificial Intelligence as the foundation for risk decisioning. It mitigates risk by making the right data available at the right time, synthesizing data using AI into risk signals, and allowing the organization to leverage a 360-degree view of the user's risk for credit decisioning.
The three pillars of AI risk decisioning are:
- Data integration, not data specialization: While single-dimensional risk scores--such as FICO--might help you get started with your credit decisioning workflows, they do not empower you to address the majority of the market that does not have sufficient credit history, thereby resulting in untapped business value. Moreover, it is unlikely that any one tool has the most comprehensive data required to make an optimal credit decision. That is the core drawback of using single-dimensional scores. AI risk decisioning offers a pragmatic alternative--data integration not data specialization: aggregating data from multiple providers and internal databases to enable the use of alternative data in credit decisioning policies.
- Combined application of Machine Learning and rules: Credit policies expressed as knockout rules are necessary for credit decisions. However, the ability of AI to synthesize large amounts of data into meaningful risk scores is paramount to scalable, fast, and accurate lending decisions. It is important to note that relying on off-the-shelf 3rd party Machine Learning (ML) models often is not effective. After all, any 3rd party tool is unlikely to have a deep understanding of your business and customer behavior. It is important to train the appropriate ML models using the right features—or data attributes—based on a combination of alternative 3rd party data sources and your internal customer data. It is worth noting that this aspect of AI risk decisioning leans heavily on the ability to aggregate and analyze data as your ML models are only as good as the data they are trained on.
- Integrated real-time analytics with decisioning: Aggregating multiple data sources is not sufficient; data must also be analyzed to derive new risk insights. For instance, a user's financial transaction history—derived from their bank information over time—is a key indicator of their propensity to pay back the credit borrowed. This entails aggregating data from the past and analyzing trends to convert that into one of the risk signals used for credit decisioning. AI risk decisioning centers around the such an ability to analyze large amounts of data in real-time to enable the discovery of new risk signals leading to more accurate lending decisions.
Together, these pillars serve as a technological foundation for a much more profound shift in consumer credit in the coming years.
The benefits of AI risk decisioning for lenders
To meet the needs of modern borrowers and to capture business value that has traditionally been lost, lenders need a credit decisioning process that is fast, accurate, and personalized. This requires a shift away from legacy systems and manual processes toward automation through the use of AI and real-time data analytics.
Accurate credit decisions
With the use of AI, credit decisioning can become more accurate. By aggregating data from multiple sources, analyzing it and using ML models, AI can identify patterns that humans would not be able to detect. This allows for a more comprehensive assessment of credit risk and leads to better credit decisions.
Personalized credit decisions
In addition to being more accurate, credit decisions made with AI can also be more personalized. By taking into account factors such as income, employment history, and spending habits, AI, using the right data, can provide a more tailored credit decision that is specific to the borrower. This leads to a better borrowing experience for the borrower and increased satisfaction with the credit product.
Faster credit decisions
AI can also help to speed up the credit decisioning process. By automating the gathering and analysis of data, AI can provide a credit decision in a matter of minutes, rather than days or weeks. This not only leads to a better borrowing experience for the borrower but also allows the lender to capture business value that would otherwise be lost.
Reduced cost of originations
AI-based credit scoring can reduce the cost of origination of loans by up to 40% and reduce the cost of decisioning on low-complexity cases. Also, lenders can scale their operations to new geographies and higher volumes with a marginal increase in human workforce capacity.
The benefits of AI risk decisioning for borrowers
AI risk decisioning doesn’t just benefit lenders; it has a profound impact on the opportunities for borrowers as well.
24x7, anytime, anywhere
With AI-powered automated decisioning, borrowers can avail of a 24x7 digital experience that is fast, accurate and seamless.
With end-to-end automation of the application to disbursal process, customers never upload the same documents twice - what's more, the process's outcome can often be predetermined, thereby allowing the lender to conditionally approve loan offers before the completion of an application.
Customers who are a part of the gig economy and first-time borrowers, with little to no credit history, will now be able to avail of loans based on healthy financial behaviors captured in their aggregated habits over time. Moreover, as the learning process is fine-tuned to eliminate bias, lending and business rules will become contextually sensible to buyers.
AI risk decisioning is the future of credit underwriting
It is clear that to meet the needs of modern borrowers and to capture business value, lenders need a credit decisioning process that is fast, accurate, and personalized. This requires a shift away from legacy systems and manual processes towards automation through the use of AI. AI risk decisioning is the future of credit decisioning because it offers several advantages over traditional methods—AI risk decisioning based credit underwriting is more accurate, more personalized, and faster than traditional methods. Thus, it is evident that AI risk decisioning should be the new foundation of modern credit underwriting to enable lenders to stay ahead of the competition, capture untapped business value, and deliver a seamless borrowing experience to customers.