Today’s financial services industry is looking to minimize risk and maximize opportunity. In order to have better insight toward these goals, banks can tap into financial data analytics. Being able to track and manage global data allows banks to provide a better overall customer experience, as well as assess risk and opportunity more accurately.
Big data can illuminate risk by making it possible to calculate an overall risk position virtually in real time. Many financial institutions rely on an overnight or 12-hour cycle to calculate risk positions based on models and other information. This means that for intraday trading and transactions, they are always working with relatively stale data. This may not be a problem on normal days, but when the market is especially volatile—and every market will behave unexpectedly from time to time—using stale data is like flying blind. Absorbing multiple streams of information and using the considerable computational power most data platforms provide makes it possible to calculate risk models much more frequently.
In addition, big data can help with satisfying compliance priorities and requests from regulators. After all, if a financial institution is reporting a number to a regulator, it must be able to defend that number. An institution should be able to answer questions about what went into calculating that number and how good the data behind it is. For instance, Santander UK has a customer data lake and a technical data lake that consist of more than 40 million customer records alongside billions of transaction records. Processing, searching, and understanding that data allows Santander UK to minimize risk in a way that was impossible without a data platform.
Finally, big data assists in credit card fraud detection. It can help businesses find the “sweet spot” between two opposite requirements: maximizing fraud detection and minimizing false positives. Of course, there is a huge amount of fraud, but declining every transaction that looks in any way suspicious can lead to a horrible customer experience. Big data can change that.
Opportunities can be hidden within data, which means data is in demand. For example, Santander UK began its data journey in 2014, and within nine months, it had already created a highly available, real-time, customer-facing analytics application. Santander’s data platform grew so much in terms of content, use cases, workforce, and overall use that the bank continues to explore alternative setups to manage and make the most of its data. Harnessing data maximizes opportunity, and that opportunity comes quickly, forcing companies to adapt.
Many companies are starting to utilize the data they generate internally, but they’re also starting to purchase third-party data in order to generate better models. Third-party data allows companies to cross-reference their own data against outside data. For example, a company may purchase third-party geolocation data to pair with internal records of credit card transactions. This allows them to recognize potential instances of fraud. For example, if a customer physically swipes a card in New York City, and then 15 minutes later in San Francisco, that must be a fraudulent transaction. On the other hand, the same combination can also provide insights into a client’s travel patterns and behavior, which presents opportunities for cross-sales and upselling.
But credit cards aren’t the only area where financial services organizations can use big data to identify opportunity. Some organizations are using data to make investment decisions. They might use satellite and weather data, in addition to fundamental internal data, to determine whether to add to their investments.
In general, the opportunity with financial data analytics lies in increasing the fidelity of general operations models. The power of big data to help financial services companies avoid risk and capitalize on opportunity will only grow from here.
Read more about how Santander UK created a customer analytics application based on a big data platform.