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The Future of Data and Analytics in Banking

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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.

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