How AI Is Used in Finance: Trading, Risk, and Fraud Detection

AI-Driven Firms Now Execute Over 70% of U.S. Equity Market Volume

Algorithmic and high-frequency trading systems — nearly all powered by machine learning and statistical models — account for the majority of equity market trading volume in the United States. This is not a future prediction; it has been the reality since approximately 2010. The financial services industry has adopted artificial intelligence faster and more deeply than almost any other sector, driven by the volume of available data, the measurability of outcomes, and the magnitude of financial incentives for marginal improvements in prediction accuracy. Understanding where and how AI operates in finance matters for investors, regulators, and anyone interacting with financial institutions.

Fraud Detection: The Highest-Stakes Classification Problem

Financial fraud detection is the most mature and widely deployed AI application in finance. Payment processors, banks, and credit card companies use machine learning classification models to evaluate every transaction in real time — within milliseconds — and assign a fraud probability score.

Visa's fraud detection AI evaluates over 500 data points per transaction and processes 65,000 transactions per second globally. The system's precision-recall tradeoff is carefully calibrated — too many false positives mean legitimate transactions are declined (creating customer friction); too few detections mean fraud passes through. Banks collectively save an estimated $10 billion annually through ML-based fraud detection systems.

Credit Scoring and Underwriting

Traditional FICO scores use five factors (payment history, amounts owed, credit history length, credit mix, new credit). AI-based credit models use hundreds to thousands of features, including some that traditional models ignore: bank account transaction patterns, device characteristics, application timing, and behavioral data.

Fintech lenders such as Upstart have argued that their ML credit models better predict repayment than traditional FICO scores, particularly for thin-file borrowers — those with limited credit history. Upstart's 2022 SEC filings claimed their model approved 43% more borrowers than a traditional credit model while maintaining the same loss rate. This remains an area of active research and regulatory scrutiny: some alternative data features can create disparate impact on protected classes even when demographics are not explicitly included.

Algorithmic Trading and Quantitative Investing

Algorithmic trading spans a wide spectrum of strategies, from microsecond arbitrage to multi-week systematic factor investing.

• High-frequency trading (HFT): Exploits microsecond price discrepancies across exchanges using co-located servers and ultra-low-latency connectivity. Strategies include market-making, statistical arbitrage, and latency arbitrage. Profit per trade is minimal; volume is enormous.
• Quantitative equity strategies: Systematic funds (Renaissance Technologies, Two Sigma, D.E. Shaw) use ML models to identify and exploit persistent statistical anomalies — factors — in price and fundamental data. Renaissance's Medallion Fund returned approximately 66% annually before fees from 1988 to 2018 using such approaches.
• Natural language processing for alternative data: Models process earnings call transcripts, SEC filings, news articles, social media sentiment, and satellite imagery (measuring retail parking lot activity, oil tank shadow analysis) to generate trading signals before public information is fully priced in.

Robo-Advisors: Democratizing Portfolio Management

Robo-advisors use algorithms to construct and rebalance investment portfolios based on investor risk tolerance, time horizon, and goals. They automatically harvest tax losses, rebalance to target allocations, and reinvest dividends at a fraction of the cost of traditional advisors.

AI in Insurance: Underwriting and Claims

Insurance is fundamentally a risk pricing problem — exactly where ML excels. Telematics-based auto insurance products (Progressive Snapshot, Root Insurance) use driving behavior data to price policies based on actual risk. Property insurers use computer vision to analyze drone and satellite imagery for roof condition, vegetation, and property characteristics that predict claim likelihood. Claims processing AI extracts information from documents, flags suspicious patterns, and routes claims to appropriate handlers — reducing average claims processing time from days to hours.

Regulatory Challenges and Risks

• Model risk: ML models trained on historical data can fail catastrophically during novel market conditions not represented in training data — the 2010 Flash Crash, August 2015 volatility, and March 2020 COVID sell-off all exposed algorithmic fragility
• Explainability requirements: The EU AI Act and U.S. banking regulators increasingly require that credit decisions be explainable to consumers — a challenge for complex deep learning models (the "black box" problem)
• Systemic risk: When many firms use similar AI models trained on similar data, correlated failures become more likely during stress events
• Bias and discrimination: Algorithmic credit and underwriting models can encode and amplify historical discriminatory patterns even without explicit demographic inputs

The regulatory landscape for AI in financial services is evolving rapidly across the U.S., EU, and UK. The next decade will define the governance frameworks that determine how these powerful tools are deployed responsibly.