Conquering the Next Frontier in Investment Data Analytics

January 11, 2012

In the asset management technology world we are always asking, “What’s the next big thing that will change the technology landscape?” 

 In the early 2000s, everyone wanted a STP front-to-back office operation.  By the end of the decade, the buzzwords were about outsourcing middle-and-back office operations (a mix of man and machine).  So now the question everyone seems to be asking is, “what more can technology deliver to us?”

 A few days ago I was reading a fascinating article in the Wall Street Journal on “big data.” It spoke about how a handful of cutting-edge companies were helping firms comb through terabytes and pentabytes of data by applying intelligent algorithms to aggregate and identify critical insights. As I read through the article I saw how, if harnessed properly, application of intelligent algorithms could be the next big technology trend for asset management firms.

 When looking at large asset managers in the Middle East, most of the back-office operations systems have been in place for a good decade (and often much longer as you look globally).  Within these operations, every year several thousand transactions are entered and stored in back-office databases.  This information is then combined with a few thousand disjointed demographic information points from wealth management / CRM systems. And then, on top of that add historical prices, exchange rates, research notes, etc. aggregated from a multitude of sources.  You quickly end up with a system with vast quantities of data – but what good is that if you can’t harness it into useful information. I continued reading the WSJ article and did some additional research, to learn about these algorithms and how they apply to our industry.

There are several types of algorithms which are used by data analytics firms that lend themselves to asset management. First, anomaly detection algorithms are, in simple terms, a string which could be used by companies to identify whether an investment transaction is in or out of pattern, ex: does it match typical portfolio manager/trader behaviour, or not? Classification algorithms may be used to answer the question of whether this out-of-pattern activity is fraudulent or not. 

 Looking at it simply, you can see applicability to the asset management industry. From avoiding an “Adobili scandal” (UBS rogue trader) to analyzing your past data to helping understand why loss-making deals were done, these algorithms help firms learn from mistakes to avoid them in the future. Now imagine if you could insert such an algorithm into your pre-/post-trade analyses and have your middle-office comb through fewer transactions, thereby reducing your workload in the middle-office compliance function!

Then there are clustering algorithms which could be used by asset management firms to better understand client behaviour by utilizing the seemingly disjointed demographic information stored in databases. For example, what other people is this fund investor of yours most like? You could mix a clustering algorithm with a recommendation algorithm (k+nearest neighbor algo) and answer critical questions such as “what fund or ETF would this customer be most likely to invest in?”

One of the fundamental building blocks to utilizing algorithms to analyze your data in order to make better business decisions is a strong back-office with a single book of records. A single book of records provided by a product like PORTIA – with the flexibility to import and export all required data – ensures that these algorithmic systems can be easily put into place when the time is right, to give you more robust decision-making and support tools to deliver more value to your end users.

Given the power of the systems that utilize these algorithms, it is easy to see why this would be the next big leap in data and technology for asset managers. The systems are already there, being cleverly used by firms in the retail FMCG industry, credit card fraud management industry, automotive industry, etc. It is only a matter of intelligently adapting these technologies to suit our industry.

 I truly believe this could be the next frontier in the buy-side…to paraphrase from Star Trek “to explore strange new worlds (unstructured Data), to seek out new (alpha) and new (client needs), to boldly go where no man has gone before”.

– Anup Namboodiri, Sales Executive, EMEA

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