Advanced Analytics – A Wind of Change in the Banking Sector
Data is the fuel that propels the analytics solutions to generate relevant insights and predict future behavior. In that case, the banking industry is not deprived of data, with ATM machines, credit card processing, online transactions, and personal data churning out millions of data points every second. All such data were previously discarded or deemed […]READ MORE >>
Data is the fuel that propels the analytics solutions to generate relevant insights and predict future behavior. In that case, the banking industry is not deprived of data, with ATM machines, credit card processing, online transactions, and personal data churning out millions of data points every second. All such data were previously discarded or deemed unuseful. However, with the advancement in analytics capabilities, banks are using such information to uncover deeper insights. With increasing competition, new technologies such as blockchain, and looming economic instability, banks are under pressure to remain competitive and profitable without sacrificing on customer satisfaction. Although effective mining of insights has remained mostly vague in the banking industry, developments in advanced analytics and machine learning will undoubtedly re-shape the banking world. Apart from predicting customer behavior and improving risk assessment, advanced analytics can be widely used to assist decision maker to grow profits in the banking sector.
Putting a stop to anti-money laundering activities
Governments all across the world are being aggressive in their attempts to crack down on anti-money laundering (AML) activities. They have put forward AML compliance guidelines which should be followed by global and regional banks. Despite huge technology and process investments to achieve such compliance goals, the banking world is still falling short to meet these requirements resulting in million dollar fines. Today, banks all around the globe are using advanced analytics to stop the flow of such illicit funds. To ensure AML compliance, banks are using tools such as Customer Due Dilligence (CDD), Know Your Customer (KYC), social graph analysis, Transaction Monitoring Systems, behavioral modeling, and customer segmentation.
Realizing cross-selling and up-selling opportunities
Advanced analytics can be used to create a detailed profile of the customers and when coupled with transactional and trading analytics, acquisition and retention rates of the clients can be improved along with cross-selling and up-selling opportunities. With the help of predictive analytics, banks can seek out profitable customers, understand their needs, and estimate the results. Prediction models are built taking into consideration decision tree, time series, neural networks, and linear regression. Consequently, the correct product for cross-selling promotion is identified along with its optimal price point.
Digital banking and advanced analytics
Consumer expectations are growing at a rapid pace as they look for a seamless, high-quality experience across all digital channels. Financial institutions are delivering services through all possible digital mediums embedded with exciting features. It’s a win-win situation for both parties as banks can provide a much better customer experience at a fraction of the current cost. However, complications arise in the digital medium when customers start out in one channel, perform subsequent steps in other channel, and end up completing the transaction in a completely different one. As a result, banks and financial institutions are using advanced analytics to understand the customer and build a proper and consistent journey view to create a seamless multichannel experience.
Exploring growth and profit opportunities
The banking industry is continuously looking out for new growth and investment opportunities. Advanced analytics can help banks identify such opportunities and even recommend new business models. In the future, banks may partner with other industries to reap income from their data repositories. For instance, banks may be able to share customer-analytics capabilities with players in the telecom or retail industry to boost their operational efficiency. In May 2017, BBVA, a Spanish bank, officially announced its intention to launch open banking. The platform allows third parties such as retailers to use customer data to offer tailored products and services. For instance, a retailer could notify a customer when they can obtain a preapproved loan from BBVA which is accessible at the point of sale.
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