AI for Credit Modelling Use Cases

Contrary to popular opinion, getting a bank loan remains a challenge for vast populations worldwide. According to the Global Findex estimates, in 2020, 31% of people worldwide remain unbanked, meaning that around one-third of the global population has no bank accounts, credit cards, or other banking services. Though the leaders are developing countries like China and India, even in the USA, over 14 million adults remain unbanked (6% of the total population).

Access to banking services, including the right to get a loan for business development or personal purposes, is directly connected with people's socioeconomic status. Those who have no bank account and no successful loan history have minimal chances to get their loan application approved, suggesting tons of missed opportunities for business startups and developments.

One of the major contributors to such poor access to loans is the scorecard-based credit modeling system. In this paper we explore the flaws of traditional credit scoring models and examine how AI solutions can innovate the industry by offering more precise, more flexible, and predictive credit scoring results.

How Does Traditional Credit Modelling Work?

Credit risk modeling is a commonplace technique applied by financial organizations to determine specific borrowers' risk level. Parameters analyzed to determine credit risk include the individual's financial statements, default probability, loan settlement history, the user's past transactions, their age, the industry of employment, etc.

The roots of modern credit scoring were laid by Ronald A. Fisher, who offered a statistical technique of discriminant analysis in 1936. The approach was applied to differentiate between groups based on a list of measurable attributes. In 1941, Durand found this technique applicable to the determination of good and bad loans.

Thus, as the name "credit scoring" suggests, the financial institutions assign some credit scores to applicants. These scores serve as a numerical expression of the likelihood that a loan applicant will repay the debt on time and in full. Banks use credit scores to perform risk-based pricing, which means borrowers with a lower credit score are likely to receive loans on worse terms with higher interest rates.

The analysis is traditionally performed with the help of logistic regression or tree-based algorithms. For instance, the system evaluates the following factors affecting credit risk:

  1. Probability of default (a criterion formed by the credit score and debt-to-income ratio of the applicant).
  2. Loss given default (a criterion calculated based on the bank's loss in case of the borrower's default; it is evaluated for the entire portfolio of loans for a specific organization).
  3. Exposure at default (a criterion indicating the lender's risk appetite and revealing the current loss exposure rate).

As it comes from this account, traditional credit risk modeling is based on rigid customer segmentation along hard lines, such as "new customer" and "existing customer" labels. It doesn't capture subtle customer characteristics. Neither can it project the customer's income based on their industry niche, the customer's financial behavior, and a ton of additional subtle characteristics. All these features suggest poor adaptability of traditional credit risk modeling, making them unable to respond to the changing market conjuncture and consider society's mentality.

Why Is the Traditional Approach Flawed?

The greatest problem with traditional (scorecard-based) approaches to creditworthiness evaluation is their rigidity. Conventional systems are rule-based, meaning that if the individual doesn't have a flawless track record of previous loans, they will hardly get a new loan from a banking institution.

But how can an unbanked person get their first loan? And how can an individual seeking money for a business startup start earning to pay back the loan? These intricacies are not included in the classical approach to credit modeling, allowing only the banked population with a good history of loan settlement to get more loans in the future.

Based on the current estimates, the most significant flaws preventing traditional credit modeling systems from giving precise creditworthiness evaluations are as follows:

  • Analysis based on historical data only
  • Consideration of variables in an isolated manner
  • Credit scoring parameters eroded by human biases and perceptions
  • Ability to evaluate only linear relationships
  • Non-nuanced, rule-based logic
  • Reliance on structured data

AI Solutions for Smarter Credit Scoring.

The AI-based credit scoring models transform the industry of credit modeling by exposing the hidden relationships between variables that are not always evident in the rule-based systems. In contrast to the traditional systems looking at one variable at a time and weeding out the loan applicants based on their non-suitability by specific parameters, AI-powered algorithms make connections among the bits of data and let the whole dataset tell its story.

Benefits associated with AI credit scoring solutions include:

  • Greater predictive power.
  • Impressive scalability and recalibration.
  • Cost-effectiveness.
  • Vast self-learning potential.

For an AI solution to be viable for the financial sector, it must possess the following characteristics.

  1. Explainability. The model should be explainable and able to make justifiable decisions so that an external reviewer could evaluate its rationale. Explainability is a typical feature of tree-based credit scoring models that construct a hierarchy of parameters in their evaluation, showing which parameter is given more weight in the loan-related decision.

  2. Reproducibility. The model should possess the test-retest validity to explain historical decisions; for instance, if an individual didn't receive a loan a couple of years ago, the system should recalculate that historical data and give a rationale for loan refusal.

  3. Robustness. This feature of AI products requires the consistency of model performance on test sampling with the training performance without noise-related fluctuations.

  4. Diversity of models. A model should give the client versatility, being customizable to a variety of business and data types.

Let's look at how these principles were incorporated to give workable AI solutions for the banking sector.

AI Credit Scoring Use Cases.

AI can be applied in financial services in many ways. Here are a couple of use cases illustrating the unique benefits AI brings to credit scoring.

Traditional models are cumbersome because the addition of new parameters slows them down and complicates the scoring process. AI algorithms are much more dynamic in self-updates, improving over time by discarding non-efficient approaches and adding improvements without human interference.

Precise Prediction
Traditional credit scoring algorithms work linearly by analyzing historical data to produce estimates of future creditworthiness. Self-learning AI systems, in contrast, use historical and current data to improve their forecasting capacity. The advanced technical power of AI allows them to analyze big data, drawing connections between fragmented variables, and giving a much more in-depth insight into the borrower's profile. These features contribute to the steadily rising predictive potential of AI algorithms and better analysis of unstructured data.

Though many users think of AI solutions as too costly to implement, in reality, AI models' application is more cost-efficient in the long run. Most providers of scorecard-based credit scoring solutions charge the users on a per-user principle. At the same time, AI models represent an entire customizable and continuously learning system able to meet all your credit scoring and customer profiling needs. For instance, Datrics currently offers flexible ML-based credit scoring systems able to provide accurate eligibility forecasting and intelligent borrower ranking to minimize the number of potentially "bad" loans.

Analysis of Broader Datasets
Today, the recognition of new risk drivers is essential in sensitive, responsive credit risk scoring. Traditional systems can't evaluate these risks adequately, with AI serving the innovative needs better. For example, AI systems can analyze unstructured data from the customers' social media to detect risks and alarm the financial institution (e.g., posts exhibiting the customer's car damage or a fire in their house). A vital criterion of business sustainability for business lenders is what AI can capture, while humans and scorecard-based systems can't. All these aspects can be included in a self-learning AI solution to receive comprehensive and realistic evaluations of customer profiles, leading to smarter customer differentiation and credit risk calculation.

Get Your AI Credit Modelling Solution from Datrics.

When you're looking for an effective AI solution for a financial organization, the product's explainability, reproducibility, and customization matter a lot. Datrics can guarantee it all in the AI product tailored specifically for your financial business and the type of data you need to analyze.

We also offer additional flexibility by hosting the AI product either on the client's premises or in their cloud. A vital function is the ability to share the AI model (even if it is not in production), allowing access for managers or staff of the internal monitoring department. This function allows the critical staff to evaluate the model's work principles and efficiency, allowing the comprehension of its decision-making rationale.

What's more, the AI products by Datrics are scalable, able to meet your growing demands and incorporate big data. Data scientists can apply custom code to expand the system's capacity, all on our platform. Besides, Datrics keeps track of the changing market conditions and introduces adjustments into the scoring algorithms based on the current realities, e.g., the structural changes brought about by COVID-19. Thus, by acquiring our credit scoring solutions, you receive a unique chance to harness the full potential of AI credit score evaluation, never missing prospective clients, and always having the full picture.
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