Increasing regulations and scrutiny demand sophisticated analysis and monitoring to spot market malpractice and trading compliance. To achieve the robust surveillance required to ensure unbiased trading platforms, the Securities and Exchange Board of India (SEBI) chose SAS, the leader in business analytics software and services, for its investigations department.
Comprehensive data integration using SAS data warehousing and business analytics speeds investigation of suspicious transactions, boosting investor confidence.
"Pulling data into our warehouse and analyzing it the next day is our single most important task," said Avneesh Pandey, General Manager for SEBI. Pandey heads technology projects for the organization's surveillance, investigations and research departments. "With SAS, we maintain uniformity, making data easier to analyze. The result: smarter investigations for quicker results."
Committed to attention to detail and ensuring market safety, SEBI's investigators and analytics group use SAS to analyze market behavior. To handle data growth and build a tighter fraud surveillance and investigation platform, SEBI sought a robust warehouse, high-end analytics, and better predictive modeling and text mining.
"SAS offers superior data integration and management - the core of our data warehouse," said Pandey. "Data loading is crucial. We collect about 25GB of data per day and project reaching 80GB per day in two years. With SAS, we can accelerate the process of utilizing this data."
Besides addressing growing volumes of data, SAS gives SEBI a single view of customers across exchanges. SEBI can now establish relationships between market participants and generate more accurate fraud alerts based on market participants' behavior. SEBI also wanted to tap unstructured data, including analyst recommendations, blogs, annual reports, etc. to understand social media impact on investor behavior.
SEBI uses SAS Enterprise Data Integration Server to load their warehouse with disparate data from multiple exchanges countrywide. The SAS Data Quality component cleanses, standardizes and removes duplicate data to build a single-entity view of market participants.
SEBI intends to build analytical models using SAS Enterprise Miner to identify known market manipulation patterns such as circular trading, pump and dump, insider trading and front running. SAS helps to identify unknown patterns for investigators to analyze and detect any new market manipulation patterns, increasing efficiency and effectiveness in market surveillance and investigation.
"The last thing you want is to waste time chasing down things that don't turn out to be fraud," said Pandey.
SEBI views its data warehouse as an intelligent system where analytics run within the warehouse, rather than outside. With SAS Scoring Accelerator , the data warehouse processes core statistical and analytic functions, reducing movement of data and taking advantage of parallel processing. It also prevents data inconsistency while promoting better data governance.
Separately, Malaysia Building Society Berhad (MBSB) has enhanced its data mining solution with SAS Rapid Predictive Modeler, from the leader in business analytics software and services, to gain more precise insights about customers to create better products and services.
"This is an upgrade to MBSB's existing predictive analytics and data mining solution - SASEnterprise Guide and SAS Enteprise Miner - implemented in 2005," said Dato' Ahmad Zaini Othman, CEO of MBSB. "Back then, it was aimed for holistic reporting, to address high nonperforming loans (NPLs). Now, we are using campaign management and credit scorecard solutions to better measure clients' behavior and campaign penetration."
Through intensive product development and new marketing campaigns, MBSB plans to grow its retail assets -personal financing and home mortgage products - while also establishing a position as a reliable corporate financial provider.
MBSB will capitalize on its strengths as a small financial institution: agile decision making, personalized customer service, flexibility and fast turnaround time. The company does not plan to compete directly with existing financial players.
"MBSB relies on SAS solutions to realize its corporate objectives, especially assessing and approving good loans via credit risk scorecard for its retail business," said SAS Managing Director Andrew Tan. "With SAS, MBSB decision makers can execute their business plans effectively and efficiently. The new SAS Rapid Predictive Modeler solution will help MBSB uncover unknown patterns, opportunities and insights to drive proactive, evidence-based decision making."
MBSB recently introduced an array of new products, such as the MBSB Ultimate, Bijak Malaysia (Bancassurance) and the Cheeky Savings Account, to tap into underpenetrated markets, such as young professionals just beginning their careers and requiring reliable financial advice.
In addition, the credit scorecard systems will give MBSB a comprehensive customer view based on behavior variables and demographics. The dashboard reporting is essential to lowering rates of NPLs, currently 20 to 25 percent for 2011.
"The relationship between SAS and MBSB will only get stronger as MBSB continues to develop and mature with clearly defined goals and market strategies," said Dato' Zaini. "Well- established in Malaysia for 25 years, SAS has experience with risk management and data intelligence. We trust SAS will help us to develop products and offerings to successfully serve our discerning clients."