Vivek Bajaj, director of global financial services, big data and analytics, at IBM, shares this view and says that “theoretically they all have the ability to do it, but practically they all struggle pretty badly with it.”
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| [Firms must either] look to the future and innovate, or stay stuck in the past and fall behind Rupesh Khendry |
Rupesh Khendry, head of worldwide capital market solutions at Microsoft, says that while the industry has been fairly conversant with structured data, that is changing rapidly “as they shift their focus from managing transactions to becoming digital, information businesses”.
They’re all talking about the same thing: how can investment banks embrace the digital age? And they all have the same answer: they need the help of leading tech companies.
Khendry says firms are “looking to leverage unstructured information to support pre-trade analytics, manage risk, garner competitive insight, structure new products and deepen client relationships”, and so they must either “look to the future and innovate, or stay stuck in the past and fall behind”.
So in what areas specifically are these companies working with investment banks to innovate in this way?
Microsoft has provided Credit Suisse with what Khendry calls a “next generation risk management engine for the analysis of portfolio risk across global markets”, and Royal Bank of Scotland is using their data platform to process an “unprecedented level of complex queries to gain near-real-time insight into customers’ business needs as well as emerging economic trends”.
IBM’s Bajaj says “many of the trading clients we work with are very busy with how to identify alpha signals better for electronic trading. For example we are working with a large Asian securities firm which is using unstructured data – news, weather news and social feeds – as well as structured market feeds to improve their dynamic electronic trading strategies”.
RFQ response times
IBM is also working with investment banks and other financial institutions to improve the request for quotation (RFQ) response times, and with some European financial institutions “to predict hardware and software outages before they occur based on extensive trading flows that are likely to emerge”.
CVA calculations are another area where big data techniques can be applied in investment banking, says Bajaj, and equally so market surveillance to detect fraudulent trades.
Away from investment banking, he says one of the world’s largest stock exchanges, for example, is using IBM’s big data platform to scrutinize trades.
“The exchange has about 1.4 billion transactions a day being performed. It used to take them 27 hours to analyse all these individual trades for illegal trading activity,” says Bajaj. “By working with them, we helped them bring that down to two minutes.”
| Further reading |
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| Digital banking: Clouded thinking |
Speed is vital in finance, and for investment banks specifically, given the implosions during the financial crisis, being able to calculate risk exposure such as credit valuation adjustment (CVA) exposures swiftly and accurately could mean the difference between life and death.
Kilcoyne says one of the biggest challenges SAS faces with banks is “trying to liberate them from their perception of what is and what is not possible. That is to say it is actually possible to calculate value at risk on 10 million positions in a matter of minutes. This is almost like their light-bulb moment because they’ve suddenly realised that big data techniques are where the true value lies.”
Investment banks also need to align their risk assessment techniques, says Bajaj.
“What tends to happen sometimes is the CFO office will report P&L information at the end of the quarter, they then find out that the risk guys have in the meantime used different aggregation techniques and have therefore drawn different conclusions because the source data was manipulated differently.”
Liquidity risk
Liquidity risk is also a paramount concern. Ron Papanek, head of the risk metrics alternative investments business at MSCI, says banks have fallen well behind in this area.
“Liquidity analytics is a new frontier. It’s something people have been concerned with for a long time but have never had the methodology and they’ve never had the data,” says Papanek. “So we’ve developed a methodology that is multi-asset class and that addresses all instrument types. What we’re working on is collecting the data to be able to use that methodology across instruments. This is a new form of analysis that the industry desperately needs.”
At the highest level, Kilcoyne says big data, analytics and technology will transform investment banking, in two ways: “First, used properly it will enable the bank to engage with its clients more deeply; second, it will enable the bank to know itself better, which is a very important element.”