Disrupting the Financial Services Industry with data


By Mary Chen, Product Marketing, MapR

Financial services are being reshaped by technologies such as cloud, advanced analytics, artificial intelligence and big data. Cutting-edge financial institutions are using these technologies to better meet the demands of clients and regulators, while working to bolster the bottom line. But what is the state of digitisation in the financial services industry and what different ways are organisations approaching it?

Data put front and centre

In an environment where data and data analysis is everything, and countless man hours are spent analysing information, financial services firms need more efficient solutions to answering questions about loan security, risk assessment and customer satisfaction.

While the value of data and its relationship to success is well understood in financial services, most new and increasingly valuable data is unstructured or not captured by firms (dark data). Despite advancements in big data solutions, financial services have been unable to capitalise on the latest technology, because legacy systems cannot support unstructured data without adding significant IT complexity.

But where does the financial services industry stand in terms of digital maturity? Digital maturity varies among financial firms of all sizes and segments, but Morgan Stanley’s Digitalization Index indicates that the financial sector is lagging behind other industries such as retail, telecoms, and media, to name but a few.

It seems though, that with significant amounts of money on the line, we are on the cusp of wholesale digital transformation in the financial services sector. Early adopters of big data technology American Express uses machine learning to benefit its card members and business by processing petabytes of information on a single converged data platform, to better mitigate risk, personalise offers and drive efficiency.

The company has been on a mission over the last few years to incorporate a big data infrastructure with machine learning, directly into its business. This is because it realised traditional databases could no longer handle the amount of data and breadth of analytics needed to improve services and manage risk.

Machine learning applications work best when fed with large amounts of data from a wide range of sources, so moving to an enterprise-wide platform has become a top priority for firms that continue to integrate big data into everyday operations for more efficient processes.

If today the financial industry is lagging behind other sectors in digital maturity, it won’t be that way for long. The majority of CFOs across industries worldwide (58 percent) expect the amount of data they process to increase by up to 50 percent by 2020, and as per the example of American Express, digital transformation is increasingly seen as a business enabler, rather than a necessary evil.

Myriad issues met with a centralised approach to data management

Achieving the best results from a big data deployment, requires organisations to think big about what could be achieved, rather than what needs to be achieved right now.

Many financial services firms focus their technology on solving current problems but as the sector rapidly digitises, long-term goals must be taken into account as well.

Large financial firms are trying to transition from small discovery use cases to managing new volumes of data for solving problems, unlocking inefficiencies and creating new value-added services. TransUnion launched a new self-service analytics platform to help customers assess their risk strategies, while American Express uses machine learning techniques across a wide range of interactions to personalise customer offers and streamline point of sale decisions.

To move from small discovery use cases to using data enterprise-wide, financial services firms need to adopt a comprehensive approach to digitising their organisations. From digitalisation and intelligent process automation, to lead process redesign and the deployment of advanced analytics, converged data platforms provide a holistic solution to a number of digital transformation imperatives facing the sector today.

Are financial services firms ready to make that transition? Well you can judge for yourself in our second blog examining the ways in which the financial services industry is winning with big data.