Our focus on...
Risk management
To some extent, the area of risk can be seen as a natural fit for ML since it is largely a case of applying a new technique to existing data-driven decisioning processes. Use cases not fitting into the specific categories below include using ML within models predicting expected cash flows, redemptions, delinquencies, inappropriate account use (e.g., a personal account being used for business purposes), expected loss accounting models, or excess losses.
Organisational / operational
ML/AI can be used to inform trading strategies, for example, predicting purchasing decisions, or stock values and portfolio returns or by identifying portfolio allocation, optimal pricing or prudential risk management strategies. Harnessing available data is also key, for example, using real-time data to lead to more effective decisions or processing and analysing data to understand and identify highly complex relationships in the financial system and wider economy.
Enhancing customer experience and engagement
Use cases of AI applied to marketing include churn prediction leading to targeted customer retention strategies (e.g., optimisation of mortgage rates to prevent customers’ policies from lapsing). In other cases, analysing customer data and previous behaviour can be used to segment and group customers for targeted marketing offers, personalised financial offers and product recommendations
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“To minimise potential risks from AI and ML, it essential to be aware of – and incorporate! – good practice for the implementation of AI/ML use cases. ”
