AI Credit Scoring: How Machine Learning Changes Credit Decisions

Lenders now lean on smart computers to decide who can borrow money and at what price. Instead of looking only at a basic credit report, they feed a lot of numbers into machine-learning software that spots some patterns. This switch is big and growing fast. The credit-scoring business is expected to grow from approximately $20.9 billion in 2024 to around $36.7 billion by 2029. The money spent on AI across banks and other financial firms reached $43.8 billion in 2023 and is expected to surpass $50.9 billion by 2029. These rising totals show the increasing confidence lenders have in AI to improve their calculations and extend loans to more people.

Machine-learning credit scores incorporate a wide range of information, not just traditional credit card and loan data. "AI credit scoring" refers to the use of computer intelligence to assess the likelihood that someone will repay a debt. Classic models focused almost only on past bills. New systems can mix in clues from rent payments, phone apps, online shopping, and other digital footprints. Because of this richer view, loan decisions are usually faster and more personal.

How AI Credit Scoring Works

AI credit scoring starts with collecting information. The system pulls data from credit bureaus and adds other clues, such as bill payments or bank activity. After gathering the facts, it trains on past loans. During this training phase, the computer studies which patterns point to borrowers who repay and which signal trouble. In “supervised” training, it learns from cases already labeled as paid or defaulted. In “unsupervised” training, it looks for hidden patterns on its own.

After deployment, the model continues to learn and improve. When new loans are repaid or default, the system updates itself, improving future predictions. This ongoing learning is a key advantage of AI: models continually adapt as markets or behaviors change.

Alternative Credit Data in Practice

AI credit models leverage both traditional and alternative data, offering a more comprehensive view of a borrower. For example, non-traditional financial data can be sourced from various everyday sources. The two main categories of alternative data are:

Everyday bills and payments: Lenders may consider records of on-time rent payments, utilities (such as gas, electricity, and water bills), as well as streaming or phone subscriptions. Regular payments for rent or utilities show financial stability. Data on subscriptions (such as streaming video and music services) can also reveal patterns in spending. In some programs, a history of timely rental or utility payments can boost approval chances for individuals with a poor credit history.
Digital footprint signals: AI systems can look at how people use digital services. This includes a borrower's online activity (shopping history, app usage, or even the type of phone they use). For example, an applicant's social media or smartphone usage can be indirectly predictive of their behavior. These are "digital footprints" – patterns of behavior on the internet or mobile devices. AI models can utilize such signals (IP addresses, email activity, online purchasing patterns) to gain insight into a person's reliability.

Fintech and Credit Decisions

Fintech firms lead the charge. They tap fresh data and open platforms to give faster credit answers. A big breakthrough is open banking. Thanks to rules such as Europe’s PSD2, customers can let apps pull their bank details through secure links. Lenders see live balances and recent spending instead of relying on an outdated credit report, so they can decide in minutes, not days.

Open banking allows borrowers to share their financial data, which can help strengthen their case. Using open banking data can add extra points and raise a person's odds of approval. For example, a lender might immediately see a regular paycheck and low expenses from bank data, which helps validate an applicant's ability to repay.

Another big trend is buy-now-pay-later services. BNPL lets consumers make purchases in installments. Many consumers, even without credit cards, use BNPL plans. Data from BNPL accounts can also feed into credit decisions. For instance, having a strong BNPL repayment history (paying those small debts on time) is a sign of creditworthiness. Machine learning credit models can ingest BNPL data alongside other information to evaluate risk. Some credit scoring systems are even being updated to include BNPL activity by design.

Fintech credit scoring tools work especially well in fast-growing countries. In countries with low formal credit coverage, alternative signals are even more important. In parts of Asia, Africa, or Latin America, mobile phone data, utility payments, or digital wallet transactions often serve as substitutes for missing bank histories. Thanks to them, millions who were once shut out can now qualify for credit. Open banking and fast digital payments make sharing this data easy, allowing loan decisions to occur much sooner.

Traditional vs AI Credit Scoring

Traditional credit scoring uses a narrow set of inputs. It relies mostly on credit bureau data, including records of loans, credit cards, payment history, and the amount of debt. A classic model might weigh factors such as late payments, debt-to-income ratio, and length of credit history. However, AI credit scoring takes a much broader view. It can incorporate dozens or hundreds of data points, such as bank transactions and app usage.

The difference in inputs has a few key consequences:

Speed. Traditional scoring models often require manual processing and periodic updates. A lender might update a score once a month or quarter. AI models can score applicants instantly using automated software. The result is faster.
Flexibility. Old models use fixed rules (for example, subtract 20 points for a late payment). AI models are data-driven and can adjust themselves. They learn from new loan data and can adapt to changes.
Bias control. Both approaches can reflect bias. Traditional scoring can disadvantage people with thin credit or from certain groups. AI has the potential to correct some biases by adding new data sources. However, AI can also introduce bias if its data is skewed.
Transparency. Traditional credit scores follow a clear formula, so it’s easy to see how each factor counts. AI scores work differently. Their code is a “black box,” hiding the logic inside. Regulators fear these complex programs leave borrowers—and even banks—in the dark about why a loan is approved or denied.

Benefits for Lenders and Borrowers

When done right, AI credit scoring can benefit both sides of a loan with:

Better accuracy. ML can spot complex signals that a human might not notice. AI can detect how a combination of factors correlates with repayment success. Such models are more accurate than simple credit scores.
Greater inclusion. By utilizing alternative data, AI scoring can include applicants that traditional models overlook. People with limited credit history can get fairer treatment. Plaid, a fintech data provider, notes that alternative data could extend credit access to approximately 49 million U.S. adults who lack a full credit history.
Reduced defaults. AI helps lenders dodge bad loans. Its sharper math spots weak applications sooner, so fewer borrowers end up defaulting. Many systems also hunt for fraud, quickly flagging odd spending spikes or mismatched details that hint at identity theft or risky behavior.
Cost savings. Automation cuts operational costs. Lenders spend less on lengthy credit reviews and paperwork. Models that automatically score and verify applications lower labor and processing expenses. These savings can be passed to borrowers as better loan terms or to shareholders as higher margins.

Possible Risks

The new AI approach also has new concerns:

Privacy. AI scoring often uses personal data that was not included in conventional credit checks. Analyzing a person's phone usage or data from apps raises privacy issues. Large-scale data collection can cause leaks or misuse in case of improper protection. Security is a top priority because mishandling sensitive financial or digital footprint data could hurt consumers.
Biases. AI learns from whatever data we feed it, good or bad. If history shows one group being turned down for loans more often, the system can “think” that group is risky—even if those past denials were unfair. Studies show algorithms pick up signals like ZIP codes or shopping patterns that often track with race or income. Without careful checks, these hidden cues can push minorities or low-income applicants toward more rejections.
Lack of transparency. Modern machine learning models are often referred to as "black boxes." Their internal logic is so complex that even experts struggle to understand it. It makes it difficult for a borrower to understand why a loan was denied. Regulators and consumer advocates worry that without transparency, mistakes or unfair rules may go unnoticed. Consumers should still get meaningful reasons when credit is denied, even if AI was used.
Regulatory compliance. Laws like the Equal Credit Opportunity Act and the Fair Credit Reporting Act govern credit decisions. These laws require lenders to provide notice of adverse actions (denials) and reasons. AI complicates these rules.
Other risks. AI has its own weak spots. Software can break, or models built on yesterday’s data can misjudge today’s reality. If a lender leans too hard on automation, fewer people double-check the results. To stay safe, many companies still put human underwriters in the loop.

Regulation & Consumer Rights

Consumers have legal protections when it comes to credit:

Fair Credit Reporting Act (FCRA) and Equal Credit Opportunity Act (ECOA)

In the U.S., these laws protect anyone who applies for credit in the United States. Together, they say every lender must explain a rejection in clear language, not vague codes or checkboxes. That rule applies even when the decision is made by an advanced computer model instead of a human loan officer. In September 2023, the Consumer Financial Protection Bureau reminded banks that new technology is no excuse for secrecy. If an algorithm turns you down, the bank still has to tell you the specific facts that hurt your application, such as high debt or limited income.

Consumer Financial Protection Bureau (CFPB) guidelines

The CFPB, which oversees lenders, has gone beyond the law and issued hands-on guidance for AI. It tells banks to check their algorithms often to be sure they treat every applicant fairly—and to fix them if they don’t. Many lenders now run audits on their models and keep clear records of why each loan is approved or denied. Some also install “explainable AI” software that turns the computer’s logic into plain language, so borrowers get an easy-to-understand reason instead of a cryptic code.

EU and international rules

In Europe, the GDPR includes rights about automated decisions, including the right to an explanation. This means EU consumers can request that a lender clarify how an algorithm evaluated them. Similarly, emerging market regulators (India, Brazil, etc.) are drafting rules to guide AI usage in finance. Many jurisdictions are experimenting with regulatory sandboxes where firms test AI models under supervision. Open banking regulations, such as Europe's PSD2 and others in Asia, also provide secure ways to share financial data with consumer consent.

Open banking and data rights

Laws that encourage open data sharing indirectly support the use of AI in credit scoring. When consumers can easily pull their own banking or billing data into a loan application, they maintain control over their information. Most frameworks require clear consent and secure transmission. As noted by the World Bank, open finance standards and sandboxes can accelerate fair use of alternative data in credit.

Final Thought

AI credit scoring grows quickly. Market analysts forecast double-digit growth over the next decade. The AI credit scoring market should expand at a CAGR of around 26.5% between 2024 and 2029. Many more lenders, including banks, fintechs, and even retailers, will deploy AI models for lending in the years to come.

Looking ahead, several trends stand out:

More data sources. As people live more online, new signals will be incorporated into credit models. Data from smartphones (such as GPS patterns), wearable devices, or even Internet of Things (IoT) sensors could be used to inform credit decisions. Information about regular streaming subscriptions or ride-sharing habits complements traditional data.
Fairness and transparency focus. Regulators, consumers, and companies are focusing on fairness. The goal is to make AI decisions explainable and equitable. We expect to see more tools that automatically check models for bias and fairness. Some firms offer “AI advisors” that flag when an algorithmic decision might be problematic.
Regulatory innovation. Many countries are experimenting with how to regulate AI credit scoring. India's recent policy encourages banks to incorporate alternative data (such as telecom or utility records) into scorecards under a government "account aggregator" framework. Brazil, China, and several African nations have updated their credit laws to allow or encourage the use of alternative data. We may see more unified rules on exactly which data can be used and how decisions must be documented.
Embedded finance and partnerships. Fintech partnerships are likely to multiply. For example, buy-now-pay-later providers might partner with banks, sharing data to underwrite loans. Big tech and e-commerce platforms could embed credit products into apps, using integrated AI scoring behind the scenes.
Global growth. North America and Europe will remain major markets, but the fastest growth is expected in the Asia-Pacific and Latin America regions, where many consumers are considered "credit invisible." AI models utilizing mobile payments and telecom data are already prevalent in countries such as India, Indonesia, and Nigeria.