In the mid-90s, Bill Gates said that 'banking is necessary, banks are not.’ This sentiment has deepened among the population over the last decade, with public opinion turning against banks after the financial crisis of 2008 and technology opening up a range of new options for financial management. This has enabled startups to enter the sector at an unprecedented rate, causing a high level of disruption. Apple, Stripe, and Square are just a few of the companies revolutionizing how we pay for things, while digital currencies and peer-to-peer lenders are opening up new funding avenues for startups and SMEs. In a recent PricewaterhouseCoopers survey of more than 1,300 financial industry executives, 88% said they feared their business was at risk to standalone financial technology companies in areas such as payments, money transfers, and personal finance, and 51% said they believe they could lose as much as 40% of their revenue to standalone FinTech firms.
However, despite this upheaval, banks are still here, and they are still the monoliths that they were twenty years ago. In order to stay relevant, they have worked hard to harness the digital revolution and completely re-imagined their role and the customer experience, often working alongside FinTech startups to do so.
One of the main advantages that traditional banks have is the vast amount of financial data they hold about their millions of customers. They also have the structure and capital to exploit it. Speaking at the recent Google Cloud Next conference, Darryl West, Group Chief Information Officer at HSBC, explained that, ‘Apart from our $2.4 trillion dollars of assets on our balance sheet, we have at the core of the company a massive asset in [the form of] our data. And what’s been happening in the last three years is a massive growth in the size of our data assets. Our customers are adopting digital channels more aggressively and we’re collecting more data about how our customers interact with us. As a bank, we need to work with partners to enable us to understand what’s happening and draw out insights in order for us to run a better business and create some amazing customer experiences.’
The potential for data analytics is being realized across the financial sector. According to the latest Worldwide Semiannual Big Data and Analytics Spending Guide from IDC, worldwide revenues for big data and business analytics (BDA) will go up from $130.1 billion in 2016 to more than $203 billion in 2020. And it is banking that it is leading the charge, with IDC estimating that the industry spent almost $17 billion on big data and business analytics solutions in 2016.
The applications for data and analytics in banking are endless. They can use data for greater personalization, enabling them to offer products and services tailored to individual consumers in real time. For example, when you purchase an overseas flight or a car, the bank sends promotional offers of insurance to cover these products. In the future, such applications could be expanded even further. One way this could happen is if you are receiving a large bill, the bank could send a text message as you get it offering a loan to cover the cost. An algorithm would then calculate what interest rate would be most appropriate based on your historic borrowing patterns and its view of you as a credit risk, before wiring the payment across almost instantaneously.
Data will also mean that banks can more accurately gauge the risk of offering a loan to a customer. Predictive analytics models like the FICO scoring system can analyze consumers’ credit history, loan or credit applications, and other data to assess whether the consumer will make their payments on time in the future. They can also join together structured customer feedback with social media comments and other unstructured data to create a comprehensive customer profile, thus limiting exposure to risk around nonpayments.
One of the most important ways banks will be able to use their data in the future is to train machine learning algorithms that can automate many of their processes. artificial intelligence (AI) solutions that have the potential to transform how banks deal with regulatory compliance issues. According to Rahul Singh, president of financial services at IT services provider HCL Technologies, ‘We are already experiencing use-cases of AI and advance analytics in the anti-money laundering function where technology is able to bring false positives down, allowing focused approaches to risk detection and avoidance.’ A 2015 report from McKinsey & Company revealed that a dozen European banks have already moved from traditional statistical analysis modeling to machine learning, with many citing increased new product sales of 10% and churn and capital expenditure down by 20% as a result.
None of this is to say that it has been all plain sailing for big data adoption in banking. Only the largest regional and national banks (over $10 billion) last year ranked improving data and analytics capabilities among their top three priorities (47%), according to research from the Boston Consulting Group, which suggests smaller banks are behind the curve. There are many reasons for this. For one, legacy systems and siloed data are a far greater problem compared to other industries because of the complexities of their operations, and overcoming this has proved a laborious process. They also face significant challenges when it comes to attracting the best technical graduates, with many preferring the bright lights of Big Tech in Silicon Valley.
We asked three experts from the world’s leading banks what they believe the future holds for data analytics in banking.
Sreeram Iyer, Chief Operating Officer at ANZ Bank
Today, banks are building foundational platforms in areas such as Data Warehouses based on strategic reviews of what banks want to do using the Big Data. Once the establishment of organizational models is done, which is what many companies are currently going through, I don’t see banks having to spend too much time on organizational models to deal with Big Data - it will become a customary ability to handle data because it will become routine. Toolkits will be well-established and it will become something that you can track. There are new things surfacing, like Machine Learning, so that is still at an early stage of maturity. That will be a certainty in the next few years.
At the end of the day, my point is that current efforts from banks to figure out how to deal with Big Data will get sorted soon. I think technology toolkits will continue to improve. I think new areas of applications will come through, such as Robotics. Also, one can argue and debate whether Big Data will be a differentiator at all in the future, because I don’t believe it will be in a material way, since the abilities to handle it will become somewhat similar between any two institutions.
David Gledhill, Group Chief Information Officer at DBS Bank
The opportunities are vast and this is an exciting time for DBS. Over the past few years, we have made great progress. We partnered with Singapore government research agency, A*STAR, to carry out projects on data analytics to improve efficiency and increase productivity across the bank. From preventing ATM cash outs to predicting when a relationship manager would leave the bank, we have been able to harness the power of data analytics to understand patterns, predict outcomes and improve processes. These projects have been very successful.
But we have only just scratched the surface. Over 98% of the data analytics world has yet to be explored. The next step is to scale up the use of analytics.
Banks tomorrow will look fundamentally different from banks today. This is why we have spent the past few years deeply immersed in the digital agenda, whether it is changing the culture and mindsets of our people, re-architecting our technology infrastructure, or leveraging technology to improve our products and services. We hope this would make banking simpler and more efficient for our customers, so they can have more time to spend on people and things they care about.
To embrace the digital world, we are moving from a traditional banking workforce to one that is more ‘FinTech-like' and which that adopts the habits of digitally native companies. We encourage employees to ideate and develop their entrepreneurial ideas.
Nikhil Aggarwal, Executive Director and Head of FCC Analytics at Standard Chartered Bank
The biggest unexploited opportunity in Banking analytics is the lack of ‘connectivity’ within the enterprise. For example, most financial institutions have built out separate analytics practices including marketing and digital (web, social, and mobile) analytics, credit risk analytics, operations analytics, fraud analytics and compliance analytics.
These ‘siloed’ teams often leverage the same underlying data structures as they mine the data to uncover both incremental revenue opportunities and risk ‘hotspots.’ A marketing analytics team looks through the ‘lens’ of increasing activation and product penetration by cross-selling across channels. As part of this analysis, they could potentially uncover fraudulent behavior and potential anomalies in usage that may present evidence of money laundering.
Organizations need to change this myopic mindset by encouraging the formation of cross-banking analytics projects teams. This, therefore, enables the potential to form an Analytics Center of Excellence that can deliver pan-organization actionable insights, and simultaneously explore the potential of emerging technology like machine learning and cognitive computing. Similarly, solutions providers need to proactively explore and offer pan-organization solutions that address organization-wide challenges.