10 Reasons to Use AI in Credit Scoring

Traditional credit scoring is falling behind. Outdated models miss key insights, leading to higher risks and missed opportunities.

Want to improve accuracy, reduce defaults, and optimize lending? It’s time to embrace AI credit tools.

Let’s dive into the top 10 reasons why AI is the future of credit risk assessment.

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#1. Enhanced risk assessment

AI-powered tools are game-changers in analyzing massive datasets, going beyond just financial records.

AI credit scoring taps into alternative data like bill payments, online activity, mobile usage, and more.

Take RiskSeal, for example—it digs into digital footprints, analyzing:

• Social media and messenger activity
• E-commerce participation
• Paid subscription services
• Web resource utilization
• Email analysis
• Phone number verification
• Location insights

By spotting patterns and anomalies, machine learning helps lenders see more opportunities for responsible lending. 

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#2. Improved decision-making

The use of AI in credit risk management helps lenders remain competitive in today’s dynamic market. This is possible because AI-based systems can analyze information in real-time.

This approach helps credit-invisible people become visible. As a result, credit institutions issue twice as many loans. At the same time, the default rate decreases. This is exactly how the RiskSeal scoring system works.

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#3. Operational efficiency

AI-powered credit scoring streamlines risk analysis, making the process faster and more cost-effective. 

By automating assessments, financial institutions can speed up loan approvals, cut operational expenses, and handle more applications efficiently.

Take RiskSeal’s experience, for example. Our AI-driven system accelerates credit checks, enabling lenders to process applications in just seconds. 

In contrast, traditional scoring methods can take anywhere from a day to several weeks. 

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#4. Predictive analytics

According to Precedence Research, the predictive analytics market is experiencing a real boom. In 2024, it was valued at $14.4 billion, and by 2034, it is expected to grow to $100.2 billion:

This trend has not bypassed the financial sector. 

Lenders are actively leveraging the ability to predict borrowers’ future behavior based on historical data.

Modern scoring systems that combine AI and credit risk​ assessment use predictive modeling and similar techniques. 

These AI-enhanced credit models demonstrate impressive accuracy, achieving a predictive power of 0.85 AUC.

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#5. Enhanced fraud detection

Credit fraud has reached unprecedented levels. According to recent studies, 50% of banks, fintech companies, and credit unions report an increase in attempts to obtain credit fraudulently. 

25% of financial institutions have suffered losses exceeding $1 million due to such activities.

The synergy of AI and credit scoring can help address this issue. AI models show outstanding results in detecting anomalies in potential borrowers' behavior that may indicate fraudulent activities.

AI-powered scoring systems are highly effective in identity verification. Lenders can ensure that the applicant is indeed who they claim to be.

RiskSeal Digital Credit Scoring system helps lenders assess risk with smart verification tools. It uses AI Face Recognition and Name Match Analysis to confirm identity and prevent fraud.

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#6. Segmentation and targeting

Another advantage of using AI for credit unions​ is the ability to assign risk levels to borrowers and segment them based on this indicator.

With this approach, lenders can create personalized offers for each cluster of their target audience.

How does this work in practice? A credit organization can offer high-risk borrowers:

• Loan product with an increased interest rate
• Minimum loan term
• The option to obtain a loan only with collateral or a guarantor

On the other hand, a low-risk level will allow borrowers to receive loans under the most favorable conditions without additional requirements.

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#7. Regulatory compliance

The lending sector follows strict government regulations. Automating compliance checks helps lenders quickly adjust to new rules and stay compliant.

Another key regulatory requirement is ensuring transparency and explainability in machine learning models for credit risk assessment.

Black-box AI scoring systems fail to meet this standard because they provide credit decisions without revealing how they are made.

In contrast, explainable white-box AI fully complies with these regulations. The RiskSeal system uses this approach, offering clients not just assessment results but also a clear formula explaining how each decision is made.

This can be illustrated schematically as follows:

White-box AI helps credit organizations operate in compliance with regulatory requirements.

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#8. Personalization of offerings

AI can analyze vast amounts of information about potential borrowers. As a result, AI-based systems can provide lenders with comprehensive insights into the consumer.

Credit organizations can gain access to the following data:

Applicant’s purchasing habits. This includes an evaluation of the types of purchased goods, their cost, preferred payment methods, purchase frequency, and more.

Psychological profile. AI systems analyze users’ online activity to determine behavioral traits. For example, shopping at night may indicate impulsivity, while registration on online gambling platforms may suggest risk-taking tendencies.

Financial status of the borrower. Digital footprint analysis can also help assess financial stability. The use of premium services, paid subscriptions, and connections from high-end devices may indicate a steady income.

By leveraging such a wide range of data, lenders can adjust loan terms and offer the most relevant credit products. This approach increases borrower loyalty and enhances the likelihood of timely repayment.

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#9. Continuous learning and adaptation

AI-based systems can continuously improve through ongoing learning.

This enables such solutions to adapt to constantly changing external factors, including:

• Economic indicators
• Market conditions
• Consumer behavior

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#10. Reduction of human error and bias

The limitations of traditional credit scoring extend beyond its limited data set. Classic borrower assessments may also be biased due to discrimination based on factors such as:

• race
• gender
• region of residence
• …and more

Automated credit scoring eliminates such biases.

Also, AI-powered scoring systems help prevent errors caused by human factors.

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The potential of AI at RiskSeal

The use of artificial intelligence is one of the leading alternative credit scoring trends, fully supported by the RiskSeal platform.

This technology enables our clients to assess borrower creditworthiness in multiple ways:

1. AI-powered analysis and anomaly detection. The system leverages machine learning algorithms to analyze borrower behavior. It can identify unusual behavioral patterns that may indicate potential fraud.

2. Face recognition. The system compares a user's photos across various online platforms (such as social media). If discrepancies are found, the applicant is assigned a high-risk level.

3. Name matching. This verification method analyzes an applicant's name across multiple digital platforms. If inconsistencies are detected, it may signal potential fraudulent activity.

A thorough AI-driven applicant assessment allows RiskSeal clients not only to reduce default rates and increase credit approval rates but also to save operational costs.

This is achieved by identifying approximately 70% of fraudsters at the loan application stage, allowing lenders to cut expenses on Know Your Customer (KYC) verification processes.