The force awakens: data science in banking
A quiet revolution powered by the force of data science has begun to deliver significant improvements in many areas including national security, business intelligence, law enforcement, financial analysis, healthcare and disaster preparedness. But are we ready to use the force in banking? Standard Chartered’s global chief innovation officer, Anju Patwardhan, explores the topic.
Digital data has snowballed, with the proliferation of the internet, smartphones and other devices. Companies and governments alike recognise 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.
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 (BI), 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.
Banking: unleashing the power of big data
For banks – in an era when banking is becoming commoditised – the mining of big data 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 analyse big data, banks can improve, or reinvent, nearly every aspect of banking. Data science can enable hyper-targeted marketing, optimised transaction processing, personalised wealth management advice and more – the potential is endless.
A large proportion of the current big data 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.
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.
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 changes 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.
Getting ready for the big data revolution
While the potential of big data is beyond dispute, the problem for banks is that the data very often sits in large, disparate legacy systems. Making data science tools work with legacy platforms and databases sitting in silos is a huge challenge.
As organisations embrace big data, the other key challenges are mindset and finding skilled people to solve problems using the right techniques, and, ultimately, to wring out insights that can be acted upon. This requires a collaborative – almost philosophical – ongoing dialogue between the business owners and the data scientists.
Data science helps in finding correlations without going into causality but the data doesn’t just hop out and explain itself. Smart people are still required to interpret the results meaningfully.
Coping with the sheer volume of insights produced by big data presents its own set of challenges.
A virtual tsunami of data points is being thrown at today’s managers. There is simply too much information out there for knowledge workers to visualise effectively using traditional methods. Here, however, help is at hand: “advanced data visualisation”, an offshoot of the big data revolution, is the newest approach to business analytics and intelligence. Its ability to present huge, complex data sets in ways that can be read by non-experts promises to transform the way businesses – including banks – make use of number-driven insights. Artificial intelligence (AI) too is helping to pave the way.
Selectively and smartly applying new types of advanced data correlation and visualisation techniques and collaborating with the right partners – such as universities and other organisations that conduct multi-disciplinary research in this area – can open up brand new opportunities for banks.
To maintain their competitive edge, banks will need to actively identify the components of the big data trend that are the right fit for advancing their businesses. A lot – but not all – of these will prove transformational, changing the face of banking as we know it.