Data Science Innovation is Redefining Conventional Finance

The retail industry realized long ago that a data-driven approach could advance its retention and sales efforts, but also create a welcoming customer experience, and it’s far ahead in this regard when compared with other sectors. With website cookies, geofencing, store loyalty cards, and of course, purchasing data, retailers are able to reliably tell which products you’re most interested in, and optimize their relationship with you in other convenient ways. In the deployment of data for customer-centric purposes, retail is one of the more mature industries on the spectrum, but the same can’t be said of others—and finance is by far among the biggest offenders.

How many times have banks sent you irrelevant credit cards, loans, and other financial product recommendations? The number of emails and texts you’ve discarded likely number in the thousands, but in the era of big data, the variety of excuses banks can provide regarding their poor application of the big data trend are dwindling. One legitimate reason for the ongoing absence of relevant, friendly financial products and services is the sheer cost of data science for banks, which by their very nature handle enormous databases of information. Though new technologies will help banks mobilize their data better and close the gap in forthcoming years, these institutions will still need to work hard to reach a new, customer-centric plateau.

How Banks Benefit From Data Science

Data science (DS) is an aptly named field that sees analysts apply scientific methodologies to vast data sources, using it to see patterns that tell a story about those who generate this data, thus drawing relevant insights that they can act upon. With enough of the right data, businesses gain a clearer picture of their customers’ behavior and ideally, their motivations as well. Since the trend became its own discipline at the dawn of the 20th century, banks have been scrambling to collect and tame the billions of data points flowing into their systems every year.

Banks know that this trove of valuable information can help them reduce their own risk and overheads, better detect fraud, gain access to improved liquidity, and more. They’ve successfully made progress on these goals in recent years but deploying data insights for the direct benefit of customers—and not their own bottom lines (which have a shallow impact on the customer experience)—is still a largely absent idea.

New DS platforms like Endor will help banks to pave over these gaps. With an agnostic approach that can use data in any form—structured or unstructured—Endor is faster to comprehend data and is powered by a cutting-edge AI science termed ‘Social Physics’ by its creators (and Co-Founders of Endor) MIT Professor Alex Pentland and Dr. Yaniv Altshuler. An accurate way to frame Endor is that it’s the Google of predictive analytics, allowing finance professionals to ask the AI questions and receive specific answers immediately, for queries akin to “Who will need a loan tomorrow?”, “Which credit card is best for this customer?”, and “Who will call contact customer service about lines of credit?”

A reduction in the costs of human input that used to be required to answer these questions will see banks and institutional finance finally achieve parallelism with customer needs. Other new AI platforms like Feedzai help banks reduce fraud, with advanced DS capabilities designed to plug into a system and begin scoring risk on all incoming and outgoing payments. Feedzai’s groundbreaking risk engine is scalable for all stakeholders in the financial value chain—banks, acquirers, merchants, brokers, and more—and similar to Endor is capable of analyzing any type of payment and any type of data with the appropriate metrics.

The power of these new breeds of AI is shaking up banking for other reasons as well. ByteDance, one of the most groundbreaking AI startups is being inundated by offers for loans from global banks, which want to make use of its advanced AI-enabled content-serving technology. Informative and relevant content is still outside the purview of banks, who don’t often rely on this medium to draw new customers and instead leave it to affiliates or other adjacent businesses, while Bytedance will help bring this ability in-house without heavy costs.

AI Advances Rein in Costs, Make Convenience Reachable

Making sense of decades’ worth of data that banks have collected is a monumental task, but Artificial Intelligence (AI) and Machine Learning (ML) are providing new hope for banks to more quickly and inexpensively make sense of their colossal data landfills. These technologies are already progressing past peripheral decentralized platforms like blockchain when it comes to accessibility and friendly finance. Though blockchain was originally heralded as a disruptive technology that would force banks to comply with customer demands for a more independent, flexible, and accessible experience, it has since been explored by banks and yet still hasn’t delivered on its decade-old promises. Decentralized technology is slow by design, and cannot deliver the granular, real-time insights required to create accurate personalized product offers for example.

Just because data is sent to a ledger instead of a database doesn’t mean that it’s any more dissectible, and even newer AI solutions still require teams of data scientists and analysts to tweak algorithms, administer the system’s needs, and pass on answers to pertinent questions. This is why enterprising AI platforms like Endor are presaged to be the culmination of AI trends that will finally let them scale, aiding banks with recommending relevant products and services to their customers in an agile manner, among other prospective benefits.

Reinforcing Intelligence Tools for Better Business

Banks are keenly aware of the need for AI and data science, and in many ways have already mastered the ‘old guard’ of AI tools. However, they’ll need to embrace new and unique AI solutions to cover ground that is yet untrod—unobtrusive and quality relationships with their customers. It’s not enough to shave overheads and compete with each another on prices or interest rates, or to mail out broad and unspecific offers for products and services, no matter how valuable they may be. The banks of the future will be able to predict what you need with laser accuracy and therefore stop casting such wide and unwieldy nets that cause more torment than empowerment.