Commentaries and insightful analyses on the world of finance, technology and IT.

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April 7, 2015

Belling The Cat - Addressing a dilemma faced by Investment Bank Risk Officers

Compliance in the trading world is more a topic for frequent discussion than proactive action. In case of investment banks, which significantly influence industry behavior, compliance is viewed more as a back-office function and hence, a cost centre. And when compared to profit centres such as frontline SBUs, the reputation of this function is a dampener for growth.

Though not publicly accepted, investment banks view many regulatory initiatives with apathy - just do what is required. Complying with the letter is more important than the spirit. In fact, if the front-office focuses on building flexible, real-time systems which are in line with stringent norms of microsecond advantage using superior process definition, technology and talent, regulatory compliance applications are built using batch processing legacy technologies. And the development team considers such assignments more as punishment posting than an elevation. In essence, effort is directed more towards 'complying'.

Although trading is supervised well from the perspective of 'front running' and helping their counter-party trader (prevalent in bond trading desks) within the investment banking, less has been achieved on employees' personal trading for a set of assets classes or individual securities. Over the years, there has been little progress on building a proactive mechanism of monitoring, reporting and possibly restricting personal trading. One reason is that monitoring personal trading significantly depends on manual processes such as paper submission using spreadsheets and investment banks lack the wherewithal to file personal trading details during an audit process. Where it exists, the process of filing external regulatory reports on employee personal trading is riddled with delays and inaccurate data attributes.

Within investment banks, employee personal trading falls under two categories - noncore and privileged. Noncore employees may not have access to privileged information and may fall in the outer layer. Privileged employees have privileged access to information related to the material interest of the bank. Such information has leveraging potential from the personal gain perspective. And while there are laws in place to closely monitor these conflicts of interests, many of the disputes between SEC / FSA and investment banks involve individual interests. These experiences form the basis for arguments to separate the research department from the investment banks.

Within an investment bank, the personal trading compliance process follows four distinctive phases:

1. Restricted list watch: Restricted list of securities are predefined and broadly communicated to employees who matter from the perspective of privileged access. This list restricts employees trading in securities where the bank has built the holding and calls for mandatory disclosure. Depending on the holding percentage which may vary from country to country, it is the bank's responsibility to maintain and update the restricted list to avoid any conflict of interest. FSA in the UK, as per rule number 7.3, checks for possible conflict of interest which includes front running (staff deals ahead of investors in the securities based on privileged access). Similarly, SEC 17j-1, rule 204-A-1 calls for the employee to obtain a duplicate brokerage statement and submit it to their employer bank.

2. Pre trade clearance: Banks have a list of 'what not to buy'. However, pre trade clearances are obtained through e-mails or by signing paper documents and often, this is done post the trade. The delay in correspondence between the risk office and an employee often results in a breach of code of conduct. Eventually, these breaches find their way into audit reports and draw the attention of regulators.

3. Broker confirmation: Though many employees diligently submit their confirmation duplicate to the bank, it is generally filed with the individual employee's records. There is hardly any automated process to reconcile the various broker confirmation receipts that an employee files from time to time. Tracing back to the point of any breach of trust is not only time consuming but also manual which means there is scope for human error.

4. Documentation: This is one of the weakest links in the chain. Poor documentation of an employee's personal trading history affect the firm's ability to pin point where the blame lies. From a compliance perspective, gathering information from various sources to synthesize and then arrive at a meaningful conclusion is still challenging.

Emerging regulations across the globe clamor for a different approach. Considering the external stimulation and more awareness on the need for better conduct internally, investment banks are looking for solutions that will enable them to stay informed and track employee personal trading to the spirit of the laws rather than the letters. Essentially, this requires behavioral changes at an employee level. But automating the process of gathering and creating reports on personal trading compliance would reduce the number of questions raised by the auditor in the short-term, and help in brand building in the long-term.

April 6, 2015

Is Big Data Ready For Consumer Banking?

Is big data just a buzzword?

Big data has been a popular buzzword in the banking industry for some time. Banks that are always on the forefront of technological innovation have long recognized the need for harnessing the information captured daily through hundreds and millions of customer transactions and interactions. As competition becomes intense and need for customer engagement becomes the bedrock for sustainability, banks are desperately looking for help from technology to extract maximum value from their core data assets.

Over the past decade, banks have closely observed the development and successful deployment of big data solutions by new-age enterprises like  Google, Amazon, Facebook, and Linkedin, enabling them to provide highly personalized and immersive user experience. Banks have waited for this technology to mature and become commercially available to take it to the next frontier of innovation in the financial industry. So is big data now ready to meet expectations of the banking industry?

 Can big data scale up to meet expectations from banks?

Let's look at key challenges faced by banks today.

1. More regulations mean banks need to store more data for a longer period of time. Banks have a problem with the archival and timely retrieval of this data that sometimes runs into terabytes. Big data provides a cost-efficient and scalable solution of storing these terabytes, or if needed even petabytes of data in Hadoop File Systems (HDFS), distributing the data across multiple commodity hardware. The Hadoop-based storage solution is horizontally scalable and many banks have already implemented this solution.

Industry news: Morgan Stanley, with assets worth US$300 billion, has started with a 15-node Hadoop cluster that the enterprise is planning to grow.

 2.  Another problem faced by most banks is the existence of data silos. Even though most banks have enterprise data warehouses (EDWs) they are expensive and don't allow the flexibility to make modifications easily. One of the fast emerging use of big data is the concept of the data lake or the logical data warehouse. The data lake acts as an enterprise repository to store data of any format, schema, and type.  It is quite inexpensive and is massively scalable solution for enterprise data needs.

The data lake can support the following capabilities:
a) Capture and store high volume of raw data across the enterprise at a fairly low cost
b) Store variety of data types in the same repository
c) Provide the ability for schema definition on read enabling generic structure for data storage

With information being available in a single place, banks can leverage association and predictive techniques on this data to generate insights about customer behavior, churn, and identify cross-selling opportunities.

To overcome the technical complexity of retrieving information from data lake, Hadoop has introduced Pig and Hive. Hive provides an SQL-like interface to the data stored in HDFS while Pig provides a high-level platform for creating MapReduce programs to process data stored in HDFS.

Industry news: HSBC implemented a Hadoop-based data lake platform to support their ongoing and future regulatory needs, thus eliminating restrictions related to data availability.

 3. The techniques described earlier process data in batches but in banking a lot of functionalities require high throughput of data. To solve this problem Apache developed Cassandra - a fully scalable distributed database system with high throughput. Many companies have benefitted from successful deployment of Apache Cassandra. The benefits include enterprises being able to identify fraudulent transactions or determine suitable offers for customer at real-time. 

Industry news: Real-time offers through online channels needed a high throughput database. Bank of America supports this high volume and high throughput data through Cassandra.

4. Big data is associated with two important capabilities - storing high data volume and generating insights. Thus, it is not only important to store these petabytes of data but also derive key business intelligence at real-time.

Apache Mahout is a library of scalable machine-learning algorithms, implemented on top of Apache Hadoop using the MapReduce paradigm. Banks can use Mahout on a huge amount of customer information stored in HDFS to have a customer 360˚ view and provide need-based customer offers.

Apache Spark provides similar functionalities in real-time as it runs in-memory in clusters. Spark analyzes data at real-time to generate time-sensitive business intelligence; for e.g., identifying fraud based on outlier behavior pattern or providing location-based offers.

Industry news: Deutsche Bank has recently implemented Apache Spark to support its real-time data needs for fraud detection.

Can banks afford to ignore big data?

We are witnessing that big data platforms are maturing rapidly to meet the demands of the financial industry. Tools are becoming less complex, reducing learning curve and resulting in the availability of more skilled personnel.

As most of these tools become commercially available, this is an ideal time for banks to invest in big data and set up the right platforms. If not, they may have to play catch-up as other industries surge ahead with the knowledge and use of big data platforms.

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