AI for Credit Risk Management: Use Cases, Challenges & Benefits

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Economic turbulence has heightened financial institutions’ concerns about extending credit to high-risk borrowers. That’s why many companies have turned to artificial intelligence in credit risk assessment and management. Adopting AI technology proves to be the most practical way for financial institutions to reduce the risk of making bad loans while simultaneously increasing the size of their customer base.

From 2018 to 2021, there was a 200% rise in the adoption of AI by financial institutions. In 2021, about 79% of high-value banks (those with over $100 billion in assets) used AI for credit risk assessment.

However, implementing AI for credit risk analytics has many practical challenges. Luckily, a professional development company, like Unicsoft, with decades of AI and ML experience, is here to help you overcome common pain points in fintech and credit management.This article describes the benefits of using AI in credit risk management and how to avoid the most common challenges of adopting it. We also describe real-world examples of companies using AI-based credit risk management.

The role of credit risk analytics in financial service organizations

Credit risk assessment and management refers to data-driven analytics that help quantify the risk of extending credit, forecast losses, and customize loan terms. It’s integral to the success of any financial service — not just the loan, mortgage, and credit industries.

  • Comprehensive credit scoring. Creditworthiness assessment helps organizations make data-driven decisions. Fintech organizations and banks make these assessments by evaluating a customer’s income, financial obligations, credit history, and behavior to determine things like the loan size, loan length, and interest rate the customer qualifies for.
  • Credit risk mitigation. Effective assessment of a customer’s financial and behavioral data allows organizations to diversify their risk and reduce the chance of borrowers defaulting on their loans. Enterprises can identify high-risk clients and risky organizations to set appropriate credit limits.
  • Financial integrity. Credit risk analytics is essential for planning a financial strategy. Companies can adhere to regulations and prepare for potential losses by diversifying portfolios, demanding collateral, or hedging.
  • Improved customer relationships. Enterprises can better understand their customer’s behavior and preferences and offer more customized loans. This approach helps increase customer loyalty, thereby encouraging repeat business and referrals.
  • Company’s credibility assurance. Quality risk assessment demonstrates financial soundness, stability, and expertise. This helps financial organizations attract more customers and partners or secure venture capital funds.

Comprehensive risk assessment can increase revenue and protect reputations, but these are not the only benefits. Leveraging fintech software development services can fuel your digital transformation, which unlocks more business advantages.

Benefits of AI in credit risk management in the financial sector

In the context of this article, artificial intelligence means computational technologies that enhance analytical and decision-making processes. In the domain of credit risk management, modern AI solutions often include subcategories of machine learning (ML) and natural language processing (NLP).

Let’s see how AI in credit risk management can enhance your business.

Accurate assessment with credit risk AI tools

AI tools can fetch third-party data from banks and payment services for a more comprehensive assessment. Then, ML algorithms then analyze the diverse information and historical data to make predictions. 

AI tools can use alternative data not found in traditional credit reports. Using business intelligence and NLP, you can evaluate customers’ creditworthiness via their internet browsing habits and social media activity.

More confident decision-making

By considering more forms of data, AI can spot correlations not detectable from traditional data alone. This provides a more nuanced view of creditworthiness, which minimizes the risk of poor loan decisions. 

For example, based on social media posts, AI software may determine that late payments on a previous loan were due to the client’s forgetfulness rather than a lack of funds. Similarly, algorithm-powered tools can analyze review platforms and news to determine if a company has a strong revenue potential and low default risk.

According to a 2021 report from PYMNTS, 63 of 100 financial institution executives surveyed said they relied on AI to make loan decisions. And 56 respondents used it for underwriting.

Real-time evaluation of creditworthiness 

Unlike humans, AI can evaluate risk in real-time. This process is called credit underwriting — using technologies to automate data collection, evaluation, and decision-making. Over 56% of the PYMNTS 2021 Report respondents employ AI precisely for automated decisions.

Advanced AI can assess a customer’s creditworthiness based on dozens of factors that change over time. Change of job, income, or taxation method can prompt the system to modify a risk profile instantaneously. Or, algorithms can continuously monitor financial markets, economic indicators, and supply chain data to evaluate a company’s creditworthiness. 

New loan opportunities

Traditional institutions hesitate to make loans to small borrowers or people without a credit history. They typically assume that the high risk of these loans and the low repayment expectancy do not justify the cost of the underwriting process. As a result, a significant pool of potential borrowers is untapped by traditional financial institutions.

With AI’s ability to predict repayment likelihood based on a broader range of data, such as social media history, companies can assess risk based on factors other than credit history. This helps create lending opportunities for a wider client base.

Enhanced credit fraud detection

In the PYMNTS survey, nearly three-quarters of the banks employing AI relied on it to manage credit risk fraud. Additionally, credit card declines cost money — even small advances in preventing false declines and ensuring efficient approvals. That’s why roughly 53% of responders of the 2022 The True Cost of Online Fraud study (by Paypal and Ponemon) considered AI and ML essential for fraud detection. 

AI technologies help remain the key use for AI in banking. It lets you flag potentially bad accounts that may not intend to pay for the credit. It also helps identify behavior that doesn’t align with a customer’s typical pattern to find discrepancies in loan applications. 

Ultimately, AI and ML help protect not only banks but also customers. Even from losses they might have inadvertently brought upon themselves by exposing their account credentials.

Reduced costs with AI for credit risk assessment

Robotic process automation and data analytics improve efficiency in the areas of processing and managing data and risk modeling (see 2021 paper “Artificial Intelligence and Bank Credit Analysis). They also help automate many mundane processes during credit assessment.

This enables financial service companies to save money on staffing and costs for manual checks. It also allows smaller companies to scale their business because they can process a larger number of credit applications and transactions.

Personalized approach to clients

Integrating AI with business intelligence tools and customer relationship management systems gives businesses a complete picture of their customers. They can segment their client base by risk profiles and preferences and tailor their marketing approach to individual clients, like processing high-priority businesses first.

AI technologies help improve the customer experience with real-time updates, notifications, and visual dashboards about their loan application review or upcoming payments. They also allow companies to personalize loans, mortgages, and other deals for appropriate clients.

To sum up, AI helps enhance credit risk assessment while making it more efficient, cutting excess spending, and enabling more personalized service. However, you should be aware of the technological challenges of using AI for credit risk assessment.

Challenges of using artificial intelligence in credit risk assessment

AI is full of nuances that require attention when developing such solutions. Let’s highlight the most important ones:

Scarcity of quality data for model training

AI models trained on inaccurate or sparse datasets provide unreliable results. Therefore, fintech institutions should consider data accuracy, validity, and relevance for credit risk assessment. One way to optimize results’ reliability and validity when real data is scarce is to train machine learning models using synthetic (artificially generated) data to supplement real-world data.

Interpretability of AI decisions

Some AI models don’t disclose the logic behind their decisions (so-called black box models). Your system won’t be as responsible if you don’t understand how the model arrived at a particular prediction.

Risk of bias when using AI for credit risk assessment

Models trained on imbalanced data can be discriminative against specific demographics. In particular, biased data in AI often amplifies unfair outcomes for minority communities.

The discriminatory potential of algorithms trained on biased data can conflict with two of the main US credit regulations — the Fair Housing Act and the Equal Credit Opportunity Act. Financial organizations should implement a framework to align their software with ethical principles. For instance, you can base it on the guidelines proposed by the US Government Accountability Office.

Strict data privacy regulations

Companies must ensure sufficient security and privacy protections when collecting, storing, and using customer data for model training and analytics. The AI algorithm must also comply with local rules outlined in regulations like the Fair Credit Reporting Act in the US, the proposed AI Act in the EU, and the international Basel III regulations (depending on the location of your customers).The principles of fairness and transparency are of the utmost importance when deploying artificial intelligence and credit risk analytics software. When correctly incorporated and trained, AI has many advantages for fintech.

Unicsoft is your saviour in implementing ai for credit risk assessment

Use cases for credit risk modeling with AI

AI is a game-changer for credit risk management and analytics. Below, we highlight how several companies have leveraged AI-powered technologies.

Risk analysis for new credit loan applicants — CreditVidya

CreditVydia, an Indian fintech SaaS offering, developed an underwriting technology that helps provide loans to over 250 million first-time borrowers without collateral or credit history. Traditional underwriting analysis denied loans to most of these customers, considering them too high-risk. 

CreditVidya’s AI underwriting solution uses nontraditional data sources — online behavior, including data about payments and data on mobile devices — in place of credit history to assess risk and propensity to repay. This technology reduced loan processing costs and improved their partner financial institutions’ loan application approval rates by 15%, and reduced their default rates.

User-friendly credit risk management — S&P Global Market Intelligence

S&P Global Market Intelligence deployed RatingsDirect on its IQ Pro platform to simplify credit risk analysis of sovereigns, securities, and credit markets. The platform includes an AI-driven search function, advanced visualization, and discovery tools that help customers find relevant research, commentary, and insights about credit risk for informed decisions.

Fraud detection solutions — PayPal

PayPal processes over 35,000 transactions per minute, so it’s unsurprising that it has experienced significant losses due to fraud. False credit card declines also led to inefficient approvals, which meant significant losses of revenue.

The company implemented machine learning to optimize the identification of red-flag patterns, restrict suspicious users, and block fraud, all without complicating the PayPal checkout process. Consequently, the company’s fraud rate (0.17–0.18% in 2019) is far below the industry average of 1.86%, translating into millions of dollars in losses averted.

Enhanced creditworthiness assessment — Capital One

Capital One has invested in ML for customer risk assessment since 2017. By integrating vast datasets into machine learning, the bank improved customer creditworthiness predictions. This turned out to be particularly helpful for clients with nonexistent credit scores. 

Their ML initiatives involve all facets of the business. For example, Capital One was one of the first major banks to integrate customer service chatbots and real-time fraud detection.

Early financial distress detection — Deloitte

Deloitte’s Eagle Eye evaluates risks early in the loan application by processing volumes of data from diverse sources, including financial reports, news, and social media activity. This helps the company highlight credit threats, alert about declining financial status, and identify bad loans.

How Unicsoft drives digital transformation in fintech

Unicsoft is a software development company with over 17 years of experience. We focus on AI solutions and the fintech market, helping companies leverage technology that improves their operations. Our recent projects demonstrate how we can help companies digitize credit risk management and enhance efficiency.

Climate-based credit risk assessment

YAPU, a Berlin-based social fintech company, sought resilient financial solutions for smallholder farmers affected by climate change. The solution was to provide real-time data based on nature-related risks, like locations, climate factors, and crop cultivation ability to assess creditworthiness of small farmers.  

The implementation was multistage. We began with a feasibility study: identified data use cases, determined data processing requirements, and selected algorithms for model training. Earlier, our team created the credit lifecycle management system and then developed the following use cases of future AI integration:

  • Credit risk assessment module that predicts the probability of a farmer going into credit default by analyzing qualitative and quantitative risk factors specific to farming credits.
  • Matching engine for reference cards that automatically appoints reference data cards to loan questionnaires. It also analyzes mistakes, data accuracy, and provides recommendations for improvement.
  • Climate-related analytics predict how nature can affect crop production and offer solutions to mitigate risks. It advises securing additional lines of credit to meet future climate needs and improve credit scores.

The credit management system helps financial companies evaluate risks faster, and more comprehensively. They can generate detailed reports that incorporate data from various sources, including credit ratings, sector and activity risks, and cash flow projections.

Digitize your business with AI in credit risk management

Despite the challenges of interpreting results, the potential for bias, and strict regulatory compliance requirements, AI is essential for effective risk management. There’s a reason why most financial companies and banks either use AI algorithms or plan to adopt them soon. Apart from credit risk assessment, AI has numerous additional fintech applications. To deepen your understanding of the subject, feel free to contact our experts.


What are some popular use cases of artificial intelligence in credit risk assessment?

AI enables real-time credit risk management by analyzing data from various nontraditional sources. Compared to traditional credit risk management, it evaluates creditworthiness more accurately, optimizes financial planning, broadens the borrower customer base, and enhances fraud detection. In addition, AI-powered automation of routine tasks also improves efficiency and reduces staffing costs.

How can you overcome the challenges of adopting AI for credit risk assessment?

Rigorous testing is essential to ensure AI models generate unbiased credit risk predictions. Training data and model development must be carefully evaluated to ensure that protected classes are represented fairly.

Companies need a compliance management system to identify and control AI/ML system risks. This involves documenting a business’s purpose in using the AI system and applying the risk management framework to non-AI systems. In addition, organizations should hire teams with AI and fair lending expertise.

What should you look for in a credit risk management system using AI?

The software should provide real-time creditworthiness evaluations for informed decision-making. Examine the accuracy of credit risk predictions regarding potential biases and prejudice in its conclusions. Make sure it integrates with your existing systems. The model should be interpretable, so you can understand its predictions. The AI software should also comply with fair-lending regulations, privacy regulations, and ethical standards.

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