Many major investment banks, such as JPMC, GS, and MS, are working on AI and ML applications. They use a variety of technologies, including machine learning, natural language processing, and computer vision, to develop their solutions.
In terms of algorithms, banks often use supervised learning algorithms. These include decision trees and random forests, for fraud detection and credit lending. They may also use unsupervised learning algorithms. Example for this is clustering and anomaly detection, for financial analytics and regulatory compliance. NLP techniques, such as sentiment analysis and topic modeling, are also commonly used in these applications.
It’s worth noting that the use of AI and ML in finance and fintech is rapidly evolving. Financial institutions are continuously experimenting with new algorithms and technologies to stay ahead of the curve. Top five trending use cases in the industry are:
- Financial Analytics Platform: Leverage machine learning, Natural Language Processing, and other AI techniques for financial analysis, algorithmic trading, and other investment strategies or tools. It also enables fraud detection or anti-money laundering (AML). For example banks have deployed AL&MI systems to analysis customer transactions, looking for unusual patterns or deviations from expected behavior.
- Credit Lending & Scoring: Use AI for robust credit lending applications. Use predictive models to uncover potentially non-performing loans and act. See the potential credit scores of your customers before they apply for a loan and provide custom-tailored plans.
- Robo-Advisory: Use AI finance chatbot and mobile app assistant applications to monitor personal finances. Set your target savings or spending rates for your own goals. Your finance assistant will handle the rest and provide you with insights to reach financial targets.
- Regulatory Compliance: Use Natural Language Processing to quickly scan legal and regulatory text for compliance issues, and do so at scale. Handle thousands of paperwork without any human interaction.
- Risk Management: Investment banks use AI and ML to monitor and manage risk, including market risk, credit risk, and operational risk. Also check my previous related blog
