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What Is Machine Learning in Finance? Machine learning is a category of artificial intelligence in which algorithms learn patterns from data rather than following explicitly programmed rules. A rule-based fraud system might flag any transaction over a certain amount; a machine learning system learns normal behaviour for each user and flags transactions that deviate from that user’s pattern.
Financial data is high-dimensional, non-linear, and constantly changing. Rule-based systems require developers to anticipate and encode patterns manually, while machine learning systems discover patterns themselves and update as new data arrives. Key ML techniques in finance: Supervised learning: Trained on labelled historical data; used for credit scoring and fraud detection. Unsupervised learning: Finds patterns without labels; used for anomaly detection and customer segmentation. Reinforcement learning: Learns by feedback from decisions; used in algorithmic trading. Deep learning: Multi-layer neural networks for time-series forecasting, document analysis, and image recognition. Must Read: AI in Investment Banking: How It Is Transforming IB in 2026 Machine Learning Fraud Detection in Finance Fraud detection is one of the clearest areas where machine learning outperforms traditional systems. Rule-based systems often generate excessive false positives and miss sophisticated fraud that does not match predefined patterns. Machine learning fraud engines analyse hundreds of variables-amount, device fingerprint, location, time, merchant category, transaction velocity, and the user’s full behavioural history-to generate a risk score in milliseconds before money moves. American Express improved fraud detection by about 6 percent using Long Short-Term Memory (LSTM) models that analyse sequential transaction patterns, and sector-wide AI systems were intercepting roughly 92 percent of fraud before approval by late 2025. Machine Learning Credit Scoring Models Traditional credit scoring relies heavily on bureau history such as repayment records and utilisation, which systematically excludes thin-file or no-file borrowers like young people or new-to-credit segments. Machine learning credit models use broader data: utility payments, bank account cash flows, income consistency, spending behaviour, mobile payment history, and employment stability. These alternative signals reveal creditworthiness that bureau data alone misses. Large Language Models are increasingly used to enhance credit risk assessment by analysing textual data-financial reports, news, and regulatory filings-so that sentiment and qualitative context complement quantitative metrics. In India, where hundreds of millions of creditworthy individuals lack formal credit histories, alternative data credit scoring is a foundational enabler of access. Fintechs such as Lendingkart, KreditBee, and Slice apply ML credit models to serve this population and directly address the credit gap. Also Read: Best Machine Learning Tools for Finance Professionals in 2026 Machine Learning Algorithmic Trading in 2026 By 2025, more than 70 percent of global hedge funds were using machine learning throughout their trading pipelines, and AI-enabled funds outperformed traditional quant funds by approximately 4–7 percent in 2024, a gap that continued into 2026.
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