SpotDy BigAI for Finance companies

Finance companies in the areas of Asset Management, Banking, Capital markets and Insurance sectors generate huge data everyday. Until now financial institutions devoted significant time and resources on data that is spread across several departments and data warehouses to be analyzed and indexed. Fortunately, Big data technology has evolved today to such extent that everything above can be done in second at faction of costs and in real time giving financial institutions real time intelligence.

What SpotDy can do for Finance institutions?

Customer data monetization

SpotDy can help financial institutions develop 360 degree view of the customer by analyzing customer profiles, spending habits, and segmentation which will develop customer risk profile, enhance customer experience and increase revenues.

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Transaction efficiency

SpotDy can help financial institutions store and prioritize feeding historical market data to trading and predictive algorithms for forecasting asset price and conduct analytics on complex securities.

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Risk Management

Global risk regulatory requirements require transactions to be stored for longer time and analyzed on real time. SpotDy can help financial institutions analyze risk across various dimensions and respond efficiently to regulatory demands.

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Use Case

A fortune 500 insurance company that offers its customers life insurance products determined that the fast moving insurance markets requires sweeping changes to its sales strategy. Ex: Auto insurance has moved online. The company traditionally depended on direct distribution of its life insurance products. Now it wants to know whether there will be a shift in customer's purchase pattern of its life insurance product to online purchases and whether the company should invest in developing the online distribution channel

There are lot of questions need to be answered to forecast the changing trends a) Which customers are most likely to gravitate toward online purchase options? b) What barriers would cutomer encounter if they were to change their purchase patterns? c) How much marketing effort would it take to make consumers feel comfortable shopping for life insurance through online? d) And what kinds of upcoming technology changes would make online sales more viable?

The company turned to big data technologies to analyse huge data sets that it had about its customers and augmented it with third party data, and modelled it for next five years to discover three potential barriers to online purchases a) that life insurance applicatications require medical underwriting, b) consumers are reluctant to share personal medical information online, and c) complexity of life insurance products.

Based on the forecast, the insurance company abandoned its plan to invest in online distribution channel and instead invested in perfecting its direct distribution channel thereby increasing its sales by $200 million.

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