Data Sciences @ Fintech Companies for Competitive Disruption & Advantage

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Long considered an impenetrable fortress dominated by a few well-known names, the banking and financial services industry is currently riding a giant wave of entrepreneurial disruption, disinter-mediation, and digital innovation. Everywhere, things are in flux. New, venture-backed arrivals are challenging the old powerhouses. Banks and financial services companies are caught between increasingly strict and costly regulations, and the need to compete through continuous innovation.

How does an entire industry remain relevant, authoritative, and trustworthy while struggling to surmount inflexible legacy systems, outdated business models, and a tired culture? Is there a way for banks and other traditional financial services companies to stay on budget while managing the competitive threat of agile newcomers and startups that do business at lower costs and with better margins? The threat is real. Can established institutions evolve in time to avoid being replaced? What other strategies can protect their extensive infrastructures and win the battle for the customer’s mind, heart, and wallet?

Financial technology, or fintech, is on fire with innovation and investment. The movement is reshaping entrepreneurial businesses and shaking the financial industry, reimagining the methods and tools consumers use to manage, save, and spend money. Agile fintech companies and their technology-intensive offerings do not shy away from big data, analytics, cloud computing, and machine learning, and they insist on a data-driven culture.

Consider a recent Forbes article by Chance Barnett, which quantifies fintech startup investments at $12 billion in 2014, quadrupling the $3 billion level achieved a year earlier. Adding to the wonderment, crowdfunding is likely to surpass venture capital in 2016 as a primary funding source. And people are joining the conversation. Barnett writes, “According to iQ Media, the number of mentions for ‘fintech’ on social media grew four times between 2013 and 2014, and will probably double again in 2015.” All of this activity underscores how technology is rattling the financial status quo and changing the very nature of money.

Yesterday’s Bank: A Rigid Culture, Strapped for Funds

Established banking institutions are strapped. The financial meltdown in 2008 questioned their operations, eroded trust, and invited punitive regulation designed to command, control, and correct the infractions of the past. Regulatory requirements have drained budgets, time, and attention, locking the major firms into constant compliance reporting. To the chagrin of some, these same regulations have also opened the door for new market entrants, technologies, platforms, and modalities—all of which are transforming the industry.

For traditional banking institutions, focus and energy for innovation are simply not there, nor are the necessary IT budgets. Gartner’s Q3 2015 forecast for worldwide IT spending growth (including all devices, data center systems, enterprise software, IT and Telecom services) hints at the challenge banks face: global IT spending is now down to -4.9%, even further from the -1.3% originally forecast, evidence of the spending and investment restraint we see across the financial landscape.

With IT budgets limited, it is hard to imagine banking firms easily reinventing themselves. Yet some are doing just that. Efficient spending is a top strategic priority for banking institutions. Many banks are moving away from a heavy concentration on compliance spending to instead focus on digital transformation, innovation, or collaboration with fintech firms. There is a huge amount of activity on all fronts. To begin, let’s review the competitive landscape of prominent fintech startups.

Data Sciences Intervention

Digital data has snowballed, with the proliferation of the internet, smartphones and other devices. Companies and governments alike recognize the massive potential in using this information – also known as Big Data – to drive real value for customers, and improve efficiency.

Big Data could transform businesses and economies, but the real game changer is data science.

Data science goes beyond traditional statistics to extract actionable insights from information – not just the sort of information you might find in a spreadsheet, but everything from emails and phone calls to text, images, video, social media data streaming, internet searches, GPS locations and computer logs.

“Data sciences enables us to process data better, faster and cheaper than ever

With powerful new techniques, including complex machine-learning algorithms, data science enables us to process data better, faster and cheaper than ever before.

We’re already seeing significant benefits of this – in areas such as national security, business intelligence, law enforcement, financial analysis, health care and disaster preparedness. From location analytics to predictive marketing to cognitive computing, the array of possibilities is overwhelming, sometimes even life-saving. The New York City Fire Department, for example, was one of the earlier success stories of using data science to proactively identify buildings most at risk from fire.

Unleashing the power of Advanced Data Mining using Data Sciences

For banks – in an era when banking is becoming commoditized – the data mining provides a massive opportunity to stand out from the competition. Every banking transaction is a nugget of data, so the industry sits on vast stores of information.

By using data science to collect and analyses Data, banks can improve, or reinvent, nearly every aspect of banking. Data science can enable hyper-targeted marketing, optimized transaction processing, personalized wealth management advice and more – the potential is endless.

A large proportion of the current Data Mining projects in banking revolve around customers – driving sales, boosting retention, improving service, and identifying needs, so the right offers can be served up at the right time.

“Data sciences can help strengthen risk management such as cards fraud detection

Banks can model their clients’ financial performance on multiple data sources and scenarios. Data science can also help strengthen risk management in areas such as cards fraud detection, financial crime compliance, credit scoring, stress-testing and cyber analytics.

The promise of Big Data is even greater than this, however, potentially opening up whole new frontiers in financial services.

Seamless experience for customers

Over 1.7 billion people with mobile phones are currently excluded from the formal financial system. This makes them invisible to credit bureaus, but they are increasingly becoming discoverable through their mobile footprint. Several innovative Fintech firms have already started building predictive models using this type of unconventional data to assess credit risk and provide new types of financing.

While banks have historically been good at running analytics at a product level, such as credit cards, or mortgages, very few have done so holistically, looking across inter-connected customer relationships that could offer a business opportunity – say when an individual customer works for, supplies or purchases from a company that is also a client of the bank. The evolving field of data science facilitates this seamless view.

Blockchain as the new database

Much more is yet to come. Blockchain, the underlying disruptive technology behind cryptocurrency Bitcoin, could spell huge change for financial services in the future. Saving information as ‘hash’, rather than in its original format, the blockchain ensures each data element is unique, time-stamped and tamper-resistant.

The semi-public nature of some types of blockchain paves the way for an enhanced level of security and privacy for sensitive data – a new kind of database where the information ‘header’ is public but the data inside is ‘private’.

As such, the blockchain has several potential applications in financial markets – think of trade finance, stock exchanges, central securities depositories, trade repositories or settlements systems.

Data analytics using blockchain, distributed ledger transactions and smart contracts will become critical in future, creating new challenges and opportunities in the world of data science.

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