Recently, we witnessed how some of the biggest financial services companies in Australia such as CommBank and AMP are battling a full-blown crisis for being on the wrong side of the regulatory and compliance norms. Post the 2008 financial crisis, the pendulum in the financial services industry is swinging between heavy regulations and lower risk. Given the dynamic nature of the regulatory landscape, financial services organisations are under constant pressure to improve their regulatory, compliance and risk management capabilities because the cost of non-compliance is far greater than its apparent monetary impact. As the regulatory and business environments become more volatile than ever before, financial services companies are feeling the increased pressure of the constantly changing regulations such as BCBS 239/RDARR, CCAR, Basel III, and Dodd-Frank. However, keeping up with these regulations places a considerable amount of pressure on risk management.
These times demands financial services companies to become vigilant and more proactive to enhance the identification, measurement, and management of their risk exposure across business lines, asset classes, and the legal entities. How can they do so? The answer lies, quite simply, in using the treasure trove of data at their disposal.
A study from PwC shows that “46 percent of banks and capital market organisations are highly data-driven in their decision-making. 38% use predictive analytics, 24% use descriptive analytics, 21% employ diagnostic analytics tools, and 16 percent use prescriptive analytics”. The report further suggests that 65% of financial services and investment management firms expect to increase spending on risk management by 5%.
Why use Big Data?
When it comes to compliance and risk management, big data analytics can be employed to adopt a proactive approach to risk management especially in the times of unforeseen financial crimes such as fraud, money laundering, high-frequency algorithm trading, etc. Regulatory compliance norms address these risks and are being implemented quickly as to match the pace with the risk components of a dynamic financial landscape. Financial services organisations need to have the right systems in place to be able to react proactively to these regulations. By leveraging the data at their disposal, financial services can calculate relevant financial ratios in real time, forecast how risk can impact organisational performance, and improve decision-making capabilities to mitigate these challenges.
Using big data analytics, financial services can navigate the challenging risk and compliance waters in the following ways:
Big Data analytics can be employed for comprehensive valuation and financial/regulatory reporting. Leveraging the data at their disposal, financial services companies can develop, deploy and maintain models for assessing and understanding market risk, credit risk, and operational risk areas in line with the regulatory norms. Using the data for all relevant data sources, such as Portfolio/Loan Accounting, Trade Capture Systems and Clearing Systems, etc. these companies can create robust data models that help in better risk modelling. With Big Data, these organisations can monitor the Complete Risk Lifecycle with advanced visualisations and scorecards for risk-based pricing, fraud detection, line-assignment, credit-risk modelling, loss forecasting, foreclosure prediction, risk-based pricing, and event modelling.
Big data comes in great use in enhancing the fraud detection capabilities of banks and financial services. These organisations can leverage data analytics to differentiate between fraudulent and legitimate transactions. Data analytics helps in uncovering new trends, patterns, fraudulent schemes, and scenarios. It also helps in calculating accurate statistical parameters to identify outliers or values that stand outside standard deviations.
Given the heavy regulatory framework that financial services organisations work in, they need robust and proactive systems for appropriate monitoring and reporting. Big Data comes to great use here. By providing transparent workflows and audit trails across geographies, it helps them identify abnormal trading patterns and maintain compliance while reducing risk.
Compliance Reports and Audit Management
Using Big Data, financial services can easily perform regulatory stress tests and build new compliance reports. However, since most of the data needed for these evaluations resides in silos and is scattered across systems, servers, apps, legal entities, etc., ensuring that the right data is used for the stress test becomes crucial. With the right analytics model that employs a converged data platform, these companies can ingest the right data from both new and legacy sources and improve the output of the annual stress test.
Given that the regulatory and compliance landscape are only going to further proliferate in complexity, financial services have to have a foolproof game in place to manage their data. Adopting the right data management techniques, ensuring the quality of data and cataloguing capabilities, maintaining right data structures, etc. are a few ways to manage changing regulations, ensure compliance to constantly evolving norms and ascertain that the firms will not struggle to achieve compliance and regulatory success. It is only by doing so that these organisations can remain competitive in a changing global market.