In my previous post, I talked about the amazing value of high-performance analytics (HPA) for tackling the enormous volume a data that banks are faced with today. Many banks have already discovered the outstanding results they can achieve by pairing advanced analytics with turbocharged technology. In today’s post, I wanted to give you a brief glimpse into what is possible when you are not limited by technology.
Let’s see what others are doing with HPA:
Supercharge Answers to Complex Questions
Large banks rely on teams of high-value modelers and business analysts to create predictive analytical models that interrogate big data sources to deliver answers that help the business make decisions. The very time-consuming process of creating, testing and evaluating each model before it goes into production often results in the delivery of only a single validated, production-ready model per day per person. Adding high-performance analytics to the model development process can supercharge the productivity of high-value staff members so they can deliver as many as 10 models per day (an impressive tenfold improvement). Even more appealing, modelers can now perform hundreds of iterations to yield models that can be 70 times more accurate. The result? Improvement in model lift can mean increased revenues through retail customer acquisition campaigns.
Make better product pricing decisions
Consider a large global payments firm that works with credit card issuers and retailers. With 1.9 billion debit and credit cards issued by more than 15,000 financial services firms – and more than 75 billion transactions valued at almost $6 trillion – the firm has data that can reveal valuable insights into consumer preferences and purchases, helping both financial services firms and retailers. Using SAS, the firm analyzes 20,000 transactions a second across 17 dimensions to understand products, clients, products by client and clients by product. Predictive analytics using big data drive this firm’s future pricing and strategy decisions in a way that could not be done before.
Avoid gridlock in credit-risk scoring, forecasts using SAS®
Imagine the advantage to a large US bank that reduces loan default calculation time from 96 hours to just four for a portfolio of more than 10 million mortgages. Now the bank can detect high-risk accounts much more quickly to forecast losses and hedge risk – plus make decisions about further lending.
Improve market, credit and liquidity risk management
What would you do with the extra time if your code ran in five minutes instead of hours or days? I want to reset how you think about business problems. I invite you to let your mind explode with ideas. Things are possible now that we could never think of before.
~ Jim Goodnight, Chief Executive Officer of SAS
With traditional computing solutions, banks typically must make transactions and then figure out afterward what their exposure was . By moving to a high-performance analytics environment that enables you to run analyses at extremely high speeds, your bank can assess the risk of a major transaction before you make it – giving you the chance to decide whether the transaction is worthwhile In other words, the speed of the analysis results in a new way of doing business.
Reduce retail home lending risk
The lending process is one of the most fundamental aspects of banking . For loans that are on a bank’s books, evaluating the probability that a borrower may default (called PD) and the expected loss to the institution in the event of a default (called LGD) are not just prudent banking measures – they are also international regulatory requirements (Basel II/Basel III) . Early detection of loans at higher risk of default is essential so bank staff can intervene more quickly with customers to make possible modifications or take other actions . For large lenders that have millions of loans outstanding, the key question is how to identify the particular loans that are beginning to be a problem but are not yet in default .
Using high-performance analytical techniques to detect these subtle changes throughout large loan portfolios can have a powerful effect. One large US bank used these techniques to reduce the time needed to identify problem loans from more than 100 hours to less than five minutes. Imagine the business effect of helping borrowers and thereby reducing charge-offs of problem loans by just one-half of 1 percent (50 basis points) . That equates to tens of millions of dollars.
Stay competitive using high-performance analytics to evaluate liquidity risk
Full-balance-sheet risk analysis for assessing liquidity demands integrating big data from multiple locations. Without integrated data, long calculation times can stretch to hundreds of hours, inhibiting timely decision making. An Asia Pacific-based bank tested high-performance analytical techniques to calculate a range of liquidity risk measures. It analyzed a portfolio of 30 million complex cash flow instruments across 50,000 different scenarios in less than eight hours. The ability to fully revaluate liquidity risk nightly ensures informed funding decisions, even in times of market volatility .