Banking / Finance

Analytics in banking services

The banking industry is data-intensive with typically massive graveyards of unused and unappreciated ATM and credit processing data. As banks face increasing pressure to stay profitable, understanding customer needs and preferences becomes a critical success factor. New models of proactive risk management are being increasingly adopted by major banks and financial institutions, especially in the wake of Basel II accord. Through Data mining and advanced analytics techniques, banks are better equipped to manage market uncertainty, minimize fraud, and control exposure risk.
According to IBM’s 2010 Global Chief Executive Officer Study, 89 percent of banking and financial markets CEOs say their top priority is to better understand, predict and give customers what they want. Financial metrics and KPIs provide effective measures for summarizing your overall bank performance.
But in order to discover the set of critical success factors that will help banks reach their strategic goals, they need to move beyond standard business reporting and sales forecasting. By applying data mining and predictive analytics to extract actionable intelligent insights and quantifiable predictions, banks can gain insights that encompass all types of customer behavior, including channel transactions, account opening and closing, default, fraud and customer departure.
Insights about these banking behaviors can be uncovered through multivariate descriptive analytics, as well as through predictive analytics, such as the assignment of credit score. Banking analytics, or applications of data mining in banking, can help improve how banks segment, target, acquire and retain customers. Additionally, improvements to risk management, customer understanding, risk and fraud enable banks to maintain and grow a more profitable customer base. The importance of these measures has been implied in the Basel II accord that explicitly emphasizes the need to embrace intelligent credit management methodologies in order to manage market uncertainty and minimize exposure risk.

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