Fintech is technology in finance. By “technology” today, I mean artificial intelligence. Artificial intelligence is revolutionising the financial landscape by enabling faster, more secure decision-making. I will explain a few use cases for AI in finance.
Real-time payment fraud detection
Real-time payment fraud detection is one of the use cases of AI in fintech. It is an automated identification of malicious financial activity within milliseconds of transaction initiation. To minimise latency, transaction and customer data must be streamed and processed by the AI model. Such a system ingests massive volumes of data and performs feature extraction in real time before settlement. Fraud detection can use anomaly detection techniques, such as isolation forests, to flag statistical outliers that deviate from established user behavioural profiles. Isolation Forest is an unsupervised, tree-based algorithm used to “isolate” unusual data points.
A Graph Neural Network (GNN) can be utilised to capture topological relationships between entities (for example, account numbers and IP addresses) that a tabular model might miss. The key challenge is to optimise the trade-off between precision (minimising false positives and customer friction) and recall (maximising detection rates and preventing fraud).
Credit default risk modelling with alternative data
Gradient-Boosting Machines (GBMs) predict loan default probability using data sources beyond traditional credit history. GBMs iteratively correct prediction errors. Scorecards translate model predictions into interpretable credit scores. It is critical to maintain explainability so users can understand why a loan was approved or rejected. The contribution of each feature towards the prediction is assessed and explained.
Anti-money laundering transaction typology discovery
Transaction graph analysis maps relationships and flows between accounts. An unsupervised clustering algorithm groups transactions by similar structural patterns, enabling the discovery of money-laundering typologies. Being an unsupervised learning methodology, it does not require labelled training data. The game is, once again, between minimising false positives and maximising detection rates.
Recommendation engines
Risk-profile-based asset allocation recommendation engines tailor portfolios to individual investor preferences by assessing risk tolerance, time horizon, and financial goals, then allocating assets across stocks, bonds, and alternatives accordingly.
Financial data analysis
This exercise focuses on classic data exploration techniques, including univariate, bivariate, and multivariate analyses, to derive insights from the underlying financial data. The decision-makers then use the insights to make decisions.
Claims fraud and triage automation
An intelligent claims management system combining natural language processing analysis of textual claim data with structured tabular attributes (demographics, amounts, claim history) can automatically route claims. Success is measured by triage accuracy (correct routing decisions) and leakage reduction (minimising fraudulent claims by passing review). Triage is the process of prioritising and sorting incoming tasks to focus on the most critical ones first.
Market sentiment analysis
Sentiment models can analyse news and social media content to extract market-relevant signals from unstructured text. These models classify content as bullish, bearish, or neutral, enabling traders and risk managers to gauge market sentiment dynamically. Event-driven analytics complements sentiment analysis by categorising critical market events, such as earnings announcements, regulatory changes, and geopolitical incidents, that trigger price movements. This integrated approach transforms disparate data sources into actionable intelligence.
AI continues to redefine the boundaries of fintech innovation by automating complex decision processes and uncovering hidden patterns in massive financial datasets. Whether preventing payment fraud, detecting money-laundering networks, optimising credit risk, or guiding investment recommendations, these systems strike a delicate balance between accuracy, transparency, and speed. The integration of advanced machine learning models, ranging from tree-based algorithms to graph neural networks, demonstrates how AI can enhance trust and intelligence in financial systems. As adoption expands, the focus must remain on ethical use, model explainability, and the safeguarding of customer data to ensure a sustainable and responsible financial transformation. The next time you pay for coffee using your Google Pay, don’t forget to thank fintech.
Disclaimer
Views expressed above are the author’s own.
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