In the year 1994, predicting that innovative technology would transform the financial sector, Bill Gates had said, ‘Banking is necessary, banks are not.’ While he courted some controversy with his provocative statement, 23 years on, it holds true for the BFSI industry and is gaining much relevance, as we speak. Advanced digital technologies have drastically changed the banking landscape, in the last two decades, with new age financial management entities disrupting traditional banking systems.
With technology creating unprecedented business avenues, financial startups are disrupting the sector at an exponential rate. Companies like Paytm, Freecharge, CitrusPay, Mobikwik have reimagined payments with digital currencies and peer-to-peer lending. As per a PWC Global FinTech Report, in a survey of 1308 financial professionals, 67% felt that disruptive financial technologies were putting their business at risk in the domain of payments, money transfers, and personal finance.
Nevertheless, in spite of the upheavals, banks still remain strong pillars of the financial world. To remain relevant in this ever-changing environment, they are working hard at harnessing digital transformation and delineate new ways to ensure exceptional customer experience. At the heart of this digital transformation, lies big data.
At the Google Cloud Next Conference held in San Francisco in March this year, Darryl West, Chief Information Officer, HSBC Bank, stated that, apart from HSBC’s $2.4 trillion assets, the core asset of the company is its data bank. West has seen enormous growth in the size of HSBC’s data assets in the last few years, with customers adopting digital transaction channels vigorously. HSBC’s strategy is to collect as much data on their customer interactions. The bank is also working closely and partnering with Fintech firms to draw insights into running a more efficient business and creating engaging customer experiences.
Slowly but surely, the financial sector is sitting up to realize the immense potential data has for business. As per IDC’s Worldwide Semiannual Big Data and Analytics Spending Guide, revenues for big data and business analytics worldwide stand at US$130.1 billion and are expected to rise to US$203 billion in 2020. With the banking sector having spent close to $17billion on big data analytics in 2016, it is clearly leading the way.
Banks and financial institutions can effectively utilize data to offer customized products and services to consumers in real time, case in point – an insurance offering on the purchase of international flight tickets. Or a loan offer via SMS to cover a large bill, at the time of the bill receipt by the customer. Based on previous borrowing patterns, a backend algorithm can calculate an appropriate interest rate, and evaluate credit risk before transferring the loan amount instantaneously.
Data is also driving the automation of work procedures and training of machine learning algorithms, as artificial intelligence and advanced analytics has the potential to transform how banks will function in the future. In Europe, most banks have transitioned to machine learning, citing a significant 10% rise in product sales and a subsequent 20% depreciation in capital expenditure, basis a 2015 McKinsey Report.
Having said that, it hasn’t always been a smooth ride for the sector when it comes to big data adoption. With only large national and regional banks prioritizing the need for data and analytics, it is clear that smaller banks and financial institutions are yet to get started or see significant benefit. One of the reasons, is age old legacy systems with data lying in silos, making this transition more difficult.
Additionally, integration and cross pollination of data is also problematic as banks build separate analytical practices such as credit risk analytics, operations analytics and compliance analytics that do not communicate with each other. More often than not, these silos study similar data structures to extricate revenue opportunities and risks. With the set-up of cross banking analytics, addressing of institution wide challenges, facilitation of actionable insights and implementation of corresponding technological solutions will be more effective and efficient.
Finally, the banking of tomorrow will be fundamentally different from the bank of today. The task ahead for financial institutions is to prioritize digital transformation and create data-centric systems. The sector needs a robust infrastructure background with measured approaches in managing critical, real time, and mobile data. Backed by dedicated investments, coherent strategies, and professional talent, the banking of tomorrow will be about understanding patterns, predicting outcomes and improving processes.
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