Predictive Analytics in Finance and Investment Banking

Predictive analytics in finance and accounting have reached a completely new level with the development of AI. Thanks to machine learning and artificial intelligence, what used to take hours now takes minutes, with extreme accuracy. How can you use predictive analytics in your operations? Find it out in this article!

Predictive Analytics in Finance, Accounting and Investment Banking: 5 Use Cases

So, how can you utilise predictive analytics to gain an advantage in finance? What should you use them for? Below, you will find several examples.

Financial Forecasting

Firstly, you can use AI-powered predictive analytics for financial forecasting. Thanks to this, you will be able to more accurately define your growth as a bank, set KPIs and OKRs and set your business on the road to success.

All you have to do here is fill the model with your data and teach it how to use one (or several) of the possible formulas used for forecasting in finance.

Risk Mitigation

Predictive analytics are also frequently employed to maintain regulatory compliance, mainly with the AML. Advanced systems fed with historical data learn the patterns indicating money laundering or scams and then compare them with current client transactions. If any of your clients shows indications of illegal activities (in progress or in the future), the system will alert you and let you react or at least increase manual monitoring.

Churn Prediction

Predictive analytics are also used in banking for churn prediction. Here, the process is similar to risk mitigation—the system learns to recognise churn indicators based on previous clients’ behaviours. When they occur, it alerts you or automatically carries out a campaign to boost customer loyalty and convince them to stay.

Algorithmic Investing

AI-powered predictive analytics are widely used in the wealth management sector of finance, and there is a good reason for that. With such technology, you can automate investing with even better precision.

Previously, algorithmic trading was based on algorithms which had their limitations. Current AI models are much better at analysing financial data, but most importantly, they can go beyond it. 

For instance, some solutions look into news feeds and juxtapose them with historical data to find connections between certain events and their financial consequences. They take much more information into account than standard algorithms and propose better-adjusted investment opportunities as a result.

Underwriting

You may also use predictive analytics for underwriting, for instance, to determine the likelihood that you will need to pay an insurance claim to your client. This way, you can make better-informed decisions and avoid situations where your services cost you more than they earn for your business.

The Takeaway

The above 5 examples are just the essence of what you can really use predictive analytics for in banking and accounting. Marketing, wealth management, administration and your financial services all benefit from predictive analytics—it’s an approach that can enhance almost all segments of your business!

You may also read: Banking Data Analytics with AI.

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