The Storage Chap / TechObsessedDad

 

Electronic Discovery and Regulatory Analytics: the next Big Data challenge for Financial Services

From retail banks to insurers and investment banks, every organisation within the Financial Services (FS) industry is struggling under the weight of ever increasing fines as they exceed the bounds of regulatory risk requirements – take the UK for example, Britain’s biggest banks have accumulated almost £50billion in charges to cover fines and lawsuits since the financial crisis.

But the costs continue to spiral. Ratings agency Standard & Poor’s predicts banks will spend another £19billion[1] as many FS organisations struggle to cope with the cost of meeting electronic discovery requirements. In some cases banks are paying up to £400m a year just to run the operations required to field queries from regulators.

Enter Big Data, the collection of key technologies and methods used to collect, process and analyse terabytes, petabytes, exabytes, even zettabytes of data, in real time. Now it has a role to play in helping financial services firms with the challenge of compliance. Specifically, Electronic Discovery and Regulatory Analytics.

To get to the point where big data analytics could start to deliver real value to financial services firms, we’ll need two things to happen.

Firstly, they need to build a better system for running search queries to demonstrate compliance. For example by using a data lake that can hold data from multiple different sources, it will be possible to run searches across different applications and events to get a proper digital picture of what occurred in a given context. i.e. “was the bank aware of a certain event at the time a trade was made?” – unstructured data in a data lake might allow financial services organisations to cross check events against email systems and other applications to prove that, if there is an investigation, the right data has been retained, and processes were followed to protect the integrity of a transaction at the right time and in the right way. This is currently a very painful and expensive manual search process for most financial institutions.

Secondly, the industry has to get ahead of the regulatory compliance issues and fines by anticipating and identifying irregular behaviour in real time i.e. predictive fraud/irregularity analytics. For example if a risk threshold is exceeded by a series of trades, the big data analytics platform underpinning the system might notice and notify the traders in question prior to completing the transaction.

However, in order to achieve this, FS organisations need to go through several key steps:

1. Build a data lake platform to hold and allow analytics on multiple data sources in real time
2. Ingest and add metadata to the data, which makes e-discovery and indexing more straightforward, addressing the e-discovery issue
3. Create a regulatory analytics system that triggers alerts whenever organisational risk thresholds are reached, alerting senior managers who can take action to curb the potential damage to the business ahead of it becoming an issue for the regulator

Worldwide, Financial Services is subject to some of the most complex regulatory requirements of any industry. With anti-money laundering and fraud directives from the EU and international bodies, additional directives like MIFID, SEPA (Single Euro Payments Area), ISAE3402 and industry standards like PCI-DSS, never mind local regulations like those in the UK around the Data Protection Act and from the FCA around maintaining proper records and potentially keeping for a certain amount of time, it’s a complex issue to solve.

We are reaching a point now where the pain and cost both of complying with electronic discovery requirements delivered by regulation and the fines incurred when banks fall foul of it are astronomically high. Responding to the changes in the financial industry with yesterday’s data capabilities will place financial institutions at a significant disadvantage.

Electronic Discovery and Regulatory Analytics is top of mind for the Big Data Solutions team here at EMC.  It would be great to hear from those tasked with addressing these challenges and to hear your experience of finding and building solutions to them – look forward to hearing from you in the comments.