By Dr. Stuart Wells, CTO of FICO
A recent study of 300 business decision makers in the UK revealed that the financial services sector scored lowest across the board when it comes to Big Data capabilities. In particular, the report from Opinium Researchfound that many organisations in the banking sector are struggling with compliance, lack of data analytics infrastructure and flexibility. Although 56% of financial decision makers confirmed Big Data’s potential to drive revenue streams and better understand customers, almost half revealed that their IT infrastructure can’t move fast enough to facilitate better use of such data. Indeed, a Forrester survey suggests most companies aren’t yet able to quickly introduce change to their operational systems, but that they are working to do better.
Prioritising Big Data
The financial services industry is under greater scrutiny than any other sector and the regulatory requirements are not going to change any time soon. However, infrastructure shouldn’t be the thing that is holding them back from making the most of industry-changing analytics – especially not in a world where you can easily access tools and infrastructure via the cloud.
Given the enormous volume of data, useful information must be separated from a lot of “noise” with high-impact business analytics. Cloud-based solutions are levelling the playing field by expanding access to Big Data analytics, infrastructure and services for organisations of all sizes. So, here are three best practices for financial services institutions to get more business value from the ever-growing volume, variety and velocity of available data.
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- Start with a business problem in mind
Exploring huge amounts of data with Hadoop and other advanced analytic tools can be lots of fun for data scientists. However, it can be a waste of time and resource if the results do not translate into something that solves real-world business problems.
To identify projects that are both promising and practical, work with business experts to understand their challenges and opportunities. It is also vital to understand the types of problems the various types of Big Data and analytic techniques can solve.
For instance, while much of the Big Data buzz has been around analysing unstructured data such as text and speech, the most important source of Big Data for most businesses is consumer transactions. Payment card, DDA/current account and loyalty program transactions produce abundant, timely streams of data. These are replete with granular details on the what, when, how much and how often of individual spending.
Until recently, most analysis of such data was done by banks and other creditors for fraud detection. Today, however, with standards-based technologies greatly reducing the cost of processing huge amounts of streaming data, transactional analysis is being adopted for a much wider range of purposes. We have helped many of our clients tap into transactional data for greater insight into both the risk and reward side of customer relationships.
- Leverage analytic innovation
Innovations in Big Data processing and analytics are transforming how businesses get value from their customer data. We’re seeing a shift from approaches that supply periodic snapshots in the form of descriptive reports and dashboards to systems that continuously analyse incoming data to produce predictions and prescriptions that are actionable in real-time.
Big Data tools and infrastructure are also making it easier to apply machine learning techniques to explore huge datasets that include a wide variety of structured and unstructured data. The right balance of these techniques with human analytic and domain expertise not only lifts business performance but also improves the ability of companies to learn at a fast pace from data-driven experiments.
Champion-challenger contests are a widely used method of improving data-driven decisions, but to accelerate learning and provide even more momentum for performance improvement, businesses must incorporate deliberate experimentation. This is the only way to introduce enough diversity into the resulting outcome data. Machine learning algorithms can help by automatically generating challenger strategies that maximise learning speed within company-specified constraints on testing cost and risk.
- Give control to the business experts
This all produces substantial value only when companies nail this final best practice. The whole point of Big Data analytics is to give business experts new insights they can quickly turn into decision strategies that ultimately improve results with customers. For instance, visual tools for building decision trees enable business experts to quickly segment customer populations using any mix of policies and data-driven insights.
One direct marketing company that deployed FICO® Analytic Modeler Decision Tree Professional was able to re-segment its customer database, extending credit to customers previously misidentified as high-risk—generating nearly $12 million in incremental sales in just four months. Meanwhile, a European bank automated its originations process, leaping from manual methods to industry best practices—including use of visual strategy development tools—and thereby transforming its forecasts. This bank will avoid the silos of information that still challenge many institutions and will support its ambitious growth plans with systems that make it easy for product marketing and risk management to collaboratively develop, test and improve operational decision strategies.
Clearly, the value of Big Data to financial services is easy to understand, but it’s not as easy to extract customer insights in time to make a difference. Fortunately, a reliable set of best practices for Big Data analytics is proving itself in industries and markets around the world and, by leveraging cloud services, companies can let a dedicated third party securely handle the underlying systems and services, paying just for the capacity and services they need. There’s no need to reinvent the wheel—just take advantage of its momentum.