Customer intelligence: Part I Why banks are turning to analytics? Thought Paper www.infosys.com/finacle Universal Banking Solution Systems Integration Consulting Business Process Outsourcing
Customer Intelligence: Part I Why banks are turning to analytics? The paradox that banks face today can be summarized in a famous quote by Rutherford D. Roger We are drowning in information and starving for knowledge. Banks today are sitting on an almost unlimited data resource to which terabytes of data gets added every day. But sadly, most of it lies unused. Analytics can help banks use this data resource to their advantage. Analytics is not an unexplored area in banking. But in the race towards embracing new technologies and alternate channels to acquire and engage customers, banks have not given due importance to data and analytics. In today s competitive banking environment, the benefits of gaining customer insights are increasing. Banks are becoming increasingly aware that the future of their business depends on what they do with their data, and more and more banks are acting on this realization by investing in technologies that help them extract value from their data resources. Wealth of information The primary source of data for analytics in banking is the information on customers and the products and services chosen by them. Fund transfers and payments performed by the customers, service requests raised by them and their preferences and interactions with the bank add to that. Apart from traditional branch banking and internet banking, innovations in the channel banking space, including mobile banking, have increased the amount of information available to banks. Social media interactions and dialogues on communities and forums also provide a heap of unstructured information. Transforming this raw data into structured inputs, eliminating duplicates and unwanted data elements and deriving intelligent insights based on customers information and banking behavior forms the crux of analytics. Types of analytics Today, analytics is performed either real-time or based on customers historical data which is available with the bank. While historical data analytics does statistical modeling of the customer data, real time analytics performs real time analysis and produces the results online based on historical data and contextual state of the customer. Offering Traveller s Insurance online to a customer who is making an online payment for a flight ticket is an example of real time analytics. Knowing the customer and the products held by the customer and using that knowledge to predict the best product that could be of interest to him is an example of historical analytics. A broad classification of analytics, is based on who the consumers of the analyzed data and reports are; ie internal consumers and external consumers. Internal consumers include bank s branch staff, relationship managers, branch managers, system administrators and selling 02 Thought Paper
agents. They would be interested in knowing customer needs, transaction volumes, the turnaround time of sales, system performance etc. External consumers include regulatory bodies, research firms and the likes. They would be interested in data including regulatory compliance, performance of the banks, comparative performance of banks etc. Different types of analytics serve different purposes. The ones that are of interest to the banking community include: Customer analytics: Analysis of customers relationship with the bank and assessing profitability and life time value form the basis of Customer Analytics. Customer segmentation, Attrition analysis and Cross- Sell Analysis are some of the key results of customer analytics. Channel analytics: Analyzing click patterns, frequently accessed services, search behavior and interactions of customers and assessing peak usage and performance of channels provide insights that can help banks fine tune services to enhance channel performance. Marketing analytics: Analyzing the success rates of marketing campaigns and the factors that led to sales opportunities would help banks devise their marketing strategy. Different customer segments prefer different channels and their responses to campaigns also differ. Understanding these preferences and responses would help in tailoring future marketing campaigns to target specific customer segments. Fraud analytics: With newer modes of banking and an ever evolving range of gadgets, fighting fraud and managing data privacy is becoming more and more difficult for Banks. Fraud Analytics helps banks in early detection of fraud, and enables them to deploy monitoring mechanisms to detect and prevent fraudulent transactions. Risk analytics: Risk Analytics help banks assess and monitor risk elements, including credit risk, market risk, operational risk and liquidity risk, and deploy measures proactively to mitigate such risks. Regulatory analytics: Configuring and capturing data in the format required for regulatory compliance and providing a simple and intuitive way to monitor it would be of great help to the compliance team for ensuring regulatory compliance. Behavioral analytics: Data regarding the interactions, search behavior and transaction behavior of different segments of customers helps banks deepen their understanding of customer preferences and enables them to serve customers better. Social analytics: Social media savvy customers and prospects leave a huge volume of data which can be used by banks. Social listening tools can analyze the data and convert it to information and insights which banks can use while designing new products and services. Regulatory analytics: Regulatory compliance is a key area for banks and financial institutions. Compliance statistics help not only the banks, but also the regulatory bodies to assess the compliance level of each bank. Performance analytics: Sales performance, branch performance, capital adequacy, liquidity analysis, asset analysis etc. are key performance indicators for banks. Performance analytics helps banks to stay competitive and offer differentiated services to customers. Predictive analytics: With data available in neat and structured formats, analytics helps banks predict the future of their business, their customers behavior, preferences and even their spend pattern. The real power of data lies in the insights that can be derived from raw information. These insights help banks predict their future and device strategies for better business results. Intelligent analytics should enable banks to pick up insights from facts. Consider the case of a customer who was issued a check book with 50 checks three months back, who writes out Thought Paper 03
3 checks per month for paying loans held with other banks. The fact is that the customer was issued 50 checks, and the insight is that 3 checks per month are being used for other bank loan repayments. A targeted campaign could be designed for such customers, offering a package to transfer their loans from other banks, with flexible repayment options. Right sell offers always bring in better results than cross sell tactics. How do banks benefit from analytics? As banks and technology vendors continue to invest in analytics, new areas and superior methods are being invented on a regular basis. Banks stand to gain a lot by embracing analytics. Analytics opens up the door to deeper understanding of customers and helps in building lasting customer relationships, devising right sell strategies, rolling out successful marketing campaigns and in reducing the risk of fraud. Analytics also hone banks ability to predict events, thereby limiting risk while improving readiness and proactivity. But it can do little on its own. The key is not only in identifying an intelligent and futuristic analytics solution, but also in entrusting the responsibility of exploiting its potential to the right set of people. Summary In the year 2015, global spending on retail banking technology is expected to cross US$ 130 billion. Banks will spend this money on technologies required in their branches, on online banking and mobile banking, and also on applications that improve efficiency, sales and service, such as analytics and business intelligence. Over the years, the banking industry has become one of the biggest spenders on analytics. While there are various analytical methods available today, a combination of historical data analysis and real time analysis of customer interactions, channel banking behavior and customer needs would improve the predictive power of banking and financial institutions. The success of banks would lie in selecting the best suite of solutions that would help them derive intelligent insights from the wealth of information available to them and in converting those insights into actions that would drive customer acquisition and engagement strategies. The next paper in this series, Layered Analytics An integrated approach, details out an integrated approach for knitting the different types of analytics. 04 Thought Paper
References 1. Gartner: Magic Quadrant for Business Intelligence Platforms 2. How Analytics Can Help Banks Navigate Financial Reform, Dr. Andrew Jennings, FICO Chief Research Officer and Head of FICO Labs, Number 43 September 2010, http://www. efma.com/efmaweb_files/file/partnerships/ Fico_Insights43_Financial_Reform.pdf 3. Retail Banks to Spend on Customer Analytics, Mobile Banking Technologies, Jan 14, 2011, http://www.networksasia.net/content/retailbanks-spend-customer-analytics-mobilebanking-technologies Arunnima B S Principal Consultant, Finacle, Infosys Sudha Annie Saigal Senior Associate Consultant, Finacle, Infosys Thought Paper 05
About Finacle Finacle from Infosys partners with banks to transform process, product and customer experience, arming them with accelerated innovation that is key to building tomorrow s bank. For more information, contact Finacleweb@infosys.com www.infosys.com/finacle 2012 Infosys Limited, Bangalore, India, Infosys believes the information in this publication is accurate as of its publication date; such information is subject to change without notice. Infosys acknowledges the proprietary rights of the trademarks and product names of other companies mentioned in this document.