Financial Services

As financial services companies digitise and get closer to the customer through improved mobile experiences, the opportunity to personalise and contextualise product and content recommendations becomes greater.

With the help of AI-powered recommender engines, financial institutions can create timely personalised experiences and next-best-offer product recommendations, creating upsell opportunities and a better ability to service the customer at lower channel costs.

While many financial institutions are already employing machine learning practises to assess credit worthiness, alternative sources of big, unstructured data are providing greater intelligence to sophisticated machine learning algorithms to drive greater accuracy and efficiency.

The growing focus on issues surrounding explainability demands development of fair and governable AI frameworks and credit decisioning engines that are bias-free across a number of dimensions, such as gender and race. Our platform Bedrock also helps to ensure that ML models continue to perform as they were intended to, with inbuilt functionality for auditability, maintenance and governance.

AI algorithms can analyse vast amounts of unstructured and cross-siloed data from the history of risk cases and identify early signs of potential malfeasance. Fraud and collections are two common use cases for machine learning within risk management.

For financial institutions trying to acquire new customers, managing risk while simultaneously keeping customer experiences hassle-free is critical. Our team can help to develop real-time, automated risk management solutions which are harmonious with customer experience and acquisition strategies.

Accurate and explainable forecasts derive key insights into how a financial institution’s performance is affected by macroeconomic conditions. Our team can help you to develop not only accurate forecasts, but dynamic and automated forecasts of thousands of individual time series, with AI driven trend and seasonality detection. This enables better decision making around customer churn, supply & demand and balance sheets.

AI can also be used to generate ideal portfolios that should be targeted by banks. Portfolio allocation optimisation sets a benchmark for how rates of return from each asset class can be optimised.

AI adoption is inhibited by issues related to lack of governance and unintended consequences. Sophisticated AI and machine learning can be a black box, but modern methods allow us to build engines that are more responsible, transparent and auditable.

The need for governance and explainability is more pertinent as it becomes necessary for financial institutions to explain the results that come out of an algorithm. Implementing AI governance frameworks and ‘Responsible AI’ toolkits can solve increasing regulatory demands and enable fairness audits on a variety of dimensions including race, gender and income.