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How Predictive Analytics Is Revolutionizing Investment Banking

Analytics is making a big impact on the industry

9Nov

The Business Insider’s recent decision to declare Goldman Sachs a ‘Tech’ Company drew consternation from many in the banking community. The online magazine argued it was doing so because the bank has 9,000 more engineers and programmers than Facebook, Twitter or LinkedIn - which doesn’t sound like the most solid of arguments. If I buy a few goldfish it doesn’t necessarily make me a pond. The attraction for Goldman Sachs of being labelled tech is obvious though; investment banks as a group don’t have the best reputation, whereas sparkly new tech companies are the darlings of the business community.

Whatever the labels, investment bank’s increasing reliance on technology is clear. Investment banks have had a lot to deal with over the last decade, with an influx of regulations coming in, and nimble new start ups - often driven by new tech - entering the market and increasing competition. The institutions that made it though the crisis intact are looking for any competitive advantage to maintain their stature at the top of the tree.

For Goldman Sachs and others, this advantage is coming from heavy investment in predictive analytics tools. Financial services institutions are data-driven by nature, and need to focus their efforts on specific operational pain points and using technology to turn undesirable information into positive outcomes. In investment banking, the amount of documents needed to accurately recommend investment or stock-purchasing behaviors is too great to process manually. The availability of big data technology means that the vast quantities of information needed can be processed without turning to spreadsheets and human inputting of numbers, which greatly reduces risks and empowers companies to make better analysis and predictions than ever before.

Asian banks in particular are leading the way in adopting predictive analytics technology. DBS, one of Singapore’s leading providers, has introduced a predictive system from IBM, the technology company, which allows staff to monitor vast reams of data and assist asset-allocation decisions for clients. They use IBM Watson Engagement Advisor to mine large volumes of complex data, including research reports and product information. They then study it against customers’ profiles to identify connections between their needs and the available information.

This kind of investment is also being seen in the West, where earlier this year Goldman Sachs invested $15 million in big data analytics start-up Kensho. Kensho’s automated technology enables Goldman’s front-office teams to query and immediately answer millions of complex questions on and around certain global market scenarios in seconds. Deutsche Bank is also making moves, and has created the new position of Head of FIC Structuring and Head of Strategic Analytics for the CB&S division - appointing Sam Wisnia, a former partner at Goldman and co-head of its technology-focused global strategies and structuring team.

This data revolution will not necessarily happen tomorrow, and there are a number of obstacles standing in the way. Jeroen Rijpkema, chief executive of international private banking at ABN Amro, notes that: ‘Big data is not just a big dream. Cognitive intelligence offers us opportunities, but it will take time for these to be translated into applicable solutions.’

To start, there are the difficulties in recruiting staff. The skills gap across the data sciences is certainly no better in the banking sector, with the skill set required particularly complex. Thomas Statnick, Global Head, Treasury and Trade Solutions Technology at Citibank, notes that: ‘It is difficult to recruit people right out of school for these positions. For big data, it is a hybrid role...business and technology. You have to train them or recruit them from business positions. It takes business, engineering and computer science skills. it is an interesting hybrid of a position, but it is really hard to find those people.’ Integration with banks’ legacy systems is another challenge that needs to be overcome. However, these are by no means insurmountable problems, and the money in investment banking means that they should be easily overcome.

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