Data applications in the financial services industry in 2015 largely focused on reducing risk, meeting regulatory objectives and bolstering fraud detection. There was also an emphasis on expanding the credit population through the use of alternative data. The merits of using big data raised questions and concerns and often resulted in more regulatory interference.
Even with this concern about increased regulations many large and small financial services organizations continue to develop strategies in 2016 to understand how big data can be used to effectively transform their organizations. It is clear that different institutions are at different stages on how to leverage the data phenomenon. Rising interest rates and regulatory roadblocks still persist and make some organizations more cautious, namely the bigger tier 1 financial firms. Smaller, perhaps more adaptable, more nimble, financial firms like regional banks, advisors, or even bureaus have a greater ability to adopt new data platforms.
Demand For Candidates
As these new data platforms begin to take hold the need for big data experts and candidates familiar with machine learning techniques will see a rise in demand for their services within the financial services realm. The process of designing and developing prepayment, default, stress testing and regulatory capital models is ever evolving. The hiring of machine learning experts will help usher in a new generation of these models. It will also play a larger role in the fraud and risk sectors of banks as mobile banking becomes more widely accepted and available. Essentially the newer big data technologies like Hadoop allow financial services leaders a way to scale and store data in a faster and more cost effective way. The newer and more reliable technologies will also give these companies a more creative way to understand their market and target potential customers/clients.
The big data market is steadily moving closer to the point where banks will need to adopt new software on a larger scale and with greater commitment than in the past. However, this transition will probably bring about more attention from the regulatory world. Perhaps the most intrusive regulatory application of big data analytics is conduct risk. Conduct risk controls how a financial institution interacts with its customers. The availability and accessibility of big data may help regulators get a bigger and better picture of what is going on inside individual banks in their relationships with their customers, but it might also allow them to implement more proactive strategies, like seeking data on phone calls and emails, to catch issues before they turn into problems.
These new customer centric and growth strategies may successfully be linked to big data by some firms, but the evolving nature of regulations pose potential roadblocks to implementing these new revenue generating strategies. It is likely that risk and regulatory data management, versus a focus on big data utilization, will still be the major challenges for financial services and credit companies in 2016.