Big Data for Banks: 5 Profitable Use Cases

Transcription

1 Big Data for Banks: 5 Profitable Use Cases

2 Did you know that one of the earliest forms of structured data as we know it, at least in Western culture, came from trade? Italian merchants saw an increase in demand for fine imports that greatly exceeded their supply. They needed a way to promise an exchange of goods for money, so the bill of sale was created. From this came paper money, and from paper money the need to closely monitor a very abstract concept: an invisible economy of money that was not, for the first time, tangibly and immediately represented by its weight in precious metal. Fast forward from the earliest ages of ledgers and bills of sale to our modern world of online transactions, of every connected device being a potential transaction point, of new currencies like Bitcoin disrupting economists and policy makers fundamental understanding of money. There s data available now about every aspect of a transaction, from the buying habits of the person spending to the likelihood that a targeted segment of people will pay their bills in full and on time. Banking data has always been a challenging form of data. Now that banks have Big Data, it s time for them to catch up to ways to best utilize the wealth of information at hand. 2

3 Businesses know that data can be transformative, but right now many are information rich and insight poor. Arleen Thomas Senior Vice President for Management Accounting and Global Markets at AICPA Share this ebook: 3

4 1 Personalized Offers and Targeted Cross-sell Large and small banks alike have a wealth of data at their disposal to understand customer and client needs in more depth. Data around when and how customers interact can help banks develop better offerings catered to specific desires and use habits. A host of factors including joining accounts, more frequent medical checkups, and other purchase patterns that indicate pregnancy could be detected automatically. Banks could act by suggesting investment and savings vehicles for college, mortgage refinancing options to cut monthly costs, and automatic bill-pay programs for added convenience. Banks are also empowered by the data they have about customers who churn from their services. If your bank is losing a whole segment of customers and is gaining many more in its place, doesn t it make sense to focus on keeping the new customer base instead of sinking tons of money into people whose behaviors indicate inevitable loss? Predictive analytics and patterns in your data allow for the power to obtain insight from today s mountain of internal and external Big Data and best target those who need the most engagement not those you re most likely to lose. 4

5 ...be it Bank of America, be it Walmart, be it Verizon, they are all data companies. You don t push cash around, it s moving bits & bytes. And we realize that, we want to be good custodians of it, & increase transparency that we have in the bank & in the larger system to drive positive change. Abhishek Mehta fmr. Managing Director for Big Data and Analytics, Bank of America Share this ebook: 5

6 2 Payment Fraud Detection, Investigation, and Prevention Banks already know general patterns of what fraudulent payments and spending look like. What if some deeper digging was possible, though? What if investigators could automatically detect unusual patterns that indicate fraud? The most crucial pain that fraud investigators feel is the crunch of time. The more time passes, the more opportunities a suspect has to potentially commit more crimes of fraud. Banks collect digital and paper trails that are perhaps fuller and more rigorous at a faster rate than any investigator at a law firm or police department. This access to bank users data presents the perfect case for mining that data for patterns that indicate fraud. Instead of forming hypotheses about what fraud looks like, banks have access to the very indicators of a good civilian and a fraudulent actor. Banks must choose to root out and investigate fraud in the data they have to keep pace with the steady increase in opportunities for fraud that are perpetuated by increased credit card use, online banking, and so on. 6

7 3 Reducing Costs and Improving Banking Efficiency A sound financial principal, however worn, is waste not, want not. While Big Data programs might seem like a hefty investment at the front end, using Big Data analytics internally at banks could drive down the cost of operations by detecting inefficiencies in a number of different functional operations across the institution. Spending less means saving more savings that could be invested in improved customer services, which could mean less churn and more profit. 7

8 As a bank, we are swimming in data, the problem is making it actionable. Emcien is not only reducing our processing time, but has increased the accuracy of our predictions with regard to both fit and power. This is allowing us to better separate signal from noise and use that signal to help our stakeholders faster. Chris Nichols Chief Strategy Officer, CenterState Bank Share this ebook: 8

9 4 Improving Customer and Internal Data Protection The number of people and businesses using online banking and investment tools grows every day in the US and around the world. The fact that so much sensitive data is being accessed on so many machines should be cause enough for banks to pay more attention to analyzing data that should be secure. The danger of online banking doesn t stop with the customer interface, though. From databases at branches that are connected to cloud servers to machine log data streaming from those servers, banks are complex institutions that use modern machinery and tools. Any time a machine is connected to the Internet, it has the chance to be hacked, and for an industry so dependent upon security and risk management, content security should be a top priority. Sensitive customer and institutional data should be monitored for suspicious activity. Network traffic and unstructured machine data can hold the patterns of suspicious activity that indicate fraud. 9

10 Becoming Customer-Centric with Automated Data Analysis Financial institutions are ahead of most industries in making use of their data, but much more can be done with the volumes of data that have been stored. Automated Data Analysis helps banks: Empower CSR/Sales teams with automatic recommendations Automatically tailor offers, rates, and terms to match each customer Analyze sentiment to identify service issues, customer complaints, and new opportunities for product offerings Preempt churn by identifying and contacting the customers most likely to defect Completely automate loyalty programs 10

11 5 Risk Management Based on Unique Risk Profiles The tenuous state of the recovering economy has left everyone feeling more cautious about investments. Banks are no different. Banks and financial institutions should, by default, have a sense of urgency about understanding data around risks. Understanding risks is at the core of both institutions. A logical choice for better managing risks is to use tools meant to handle the speed and volume at which changes in investment data happens on a daily basis. The stock market and futures are some of the first examples that come to mind when thinking of everyday big data sets. If banks and investment firms begin to take the time to employ better predictive analytics to their data, investments might be made on sounder footing. 11

12 Overcoming Barriers to Big Data Success Emcien s software provides a complete and real-time analysis of financial data. Empowered by automated data analysis, analysts are guided to the most important and actionable data, allowing them to do more with fewer resources. Emcien s solutions empower financial institutions to: Leverage stored data through automatic analysis Facilitate a customer-centric approach through data Automatically identify emerging trends from nearly any data type Data-driven risk management Automated fraud identification and investigation tools ATM service management Automatic emerging trend analysis including churn prediction and prevention Request a Live Demo Share this ebook: 12