Financial Services
Revolutionising fraud detection in banking: The impact of AI and machine learning
As industries are rapidly moving towards the digitalisation of their processes, banking and financial services are no exception. The other side of digital convenience is the constant threat of increasingly sophisticated frauds. With expanding digital transactions and financial services, traditional fraud detection systems fail to meet the demand for advanced techniques. At this point, AI fraud detection presents itself as a viable and effective solution.
Some market researchers estimate that fraudsters are already using advanced technology to commit crimes, and by 2030, the losses in the banking sector could reach a trillion dollars. Artificial Intelligence (AI) and Machine Learning (ML) are transforming financial fraud detection and supporting financial institutions to proactively identify fraudulent activities, enhance security, and protect customers.
The shift from traditional to AI fraud detection
Until very recently, banks relied on traditional methods of fraud identification, like rule-based systems. These systems flagged transactions based on predefined thresholds of ‘if, else’ definitions and known fraud patterns. Though effective to an extent, they were constrained by human errors and limited definitions of fraudulent activities, leading to frequent false positives and an incapability to detect and adapt to emerging fraud patterns.
AI-powered financial fraud detection systems analyse large volumes of transactional data in real time and identify patterns and suspicious behaviour with unprecedented accuracy. They are continuously learning from new data and detecting anomalies, giving them an edge over the traditional fraud detection system.
The role of ML in fraud detection
Fraud detection by machine learning is a dynamic approach that plays a pivotal role in banking fraud detection.
Anomaly detection
It uses advanced algorithms to identify deviations from typical transaction behaviour in real time to identify frauds.
Real-time analysis
It supports quick data analysis in a real-time environment, enabling instant decision-making to prevent fraud before it occurs.
Supervised learning models
They identify hidden fraud patterns without prior training on the exact same markers, enhancing the fraud detection system.
Unsupervised learning models
They use historical data to predict and prevent suspicious activities.
These capabilities allow banks to improve fraud detection and enhance customer trust by providing a seamless and secure banking experience.
The role of AI in fraud detection
AI fraud detection extends ML-driven analytics, offering enhanced fraud detection.
Natural Language Processing (NLP)
It automates fraud detection by analysing communication, customer behaviour, document verification, and sentiment analysis.
Deep learning
AI fraud detection works on real-time pattern recognition, sequence analysis, and complex pattern detection.
Machine learning algorithms
These algorithms work on improving accuracy in transaction categorisation, fraud detection, and processing capacity and reducing false positives.
Automated decision-making
It proactively works by instantly flagging transactions based on multiple risk factors.
AI fraud detection in banks and financial institutions makes financial fraud detection more resilient and responsive to sophisticated threats.
Limitations of AI and ML fraud detection
While AI and ML significantly contribute to improved fraud detection, they also have potential challenges.
False negatives
False negatives often occur when fraudsters bypass the AI fraud detection system or the algorithms lack training. The system may miss fraudulent transactions and patterns, allowing the miscreants unfettered access.
Data quality and bias
Fraud detection systems rely on large volumes of high-quality data to train and identify fraud accurately. Incomplete or incorrect data hampers fraud detection. Gathering and curating such a huge training dataset is a challenge. Training data that is not comprehensive and diverse may introduce bias in the model, making the experience unpleasant for customers.
Evolving fraudulent activities
Fraudsters continuously adapt to new fraud detection and prevention systems. Each time miscreants bypass the fences, AI models must be updated and trained.
Regulatory and compliance risks
Complying with banking regulations, data privacy, and other applicable laws could be cumbersome.
Implementing AI fraud detection
Fraud detection in banking requires strategic planning and execution to integrate and implement AI and ML fraud detection models effectively.
Infrastructure and system integration
Only a robust data infrastructure that integrates hardware and software can support real-time fraud detection and handle high transaction volumes efficiently.
System integration
AI models need seamless integration with the banking systems and APIs, and also require smooth data exchange mechanisms.
Model selection and training
Implementing the right ML techniques ensures continuous learning and adherence to regulatory compliances. Continuous refining of AI models with updated data and behavioural insights improves the accuracy of fraud detection.
Data quality and security
Ensuring high-quality and unbiased data supports training the algorithm while maintaining data privacy and security.
Operational efficiency
A team of AI experts and data scientists to develop, incorporate, and maintain an AI fraud detection system collaborated with fraud analysts and compliance teams to ensure operational efficiency.
How can Infosys BPM help with AI fraud detection?
As fraud tactics evolve, AI and ML will become central to security in banks and financial institutions. Infosys BPM offers banks the opportunity to leverage AI in financial services to identify suspicious transactions, pattern recognition, data integration, and loss prevention to protect assets and enhance customer experience.
