Leverage Analytics to Boost Operational Risk Management
Wealth management and investment management teams are under increased pressure to perform combined analytics as part of operational risk management. This is due to demands for analytical rigor from regulatory authorities such as FINRA. We helped a US bank to proactively identify and manage bad actor risk, improve name screening for AML monitoring, and maintain financial advisor compliance.
Traditionally, the operational risk and compliance management teams in large banks have operated in a fairly siloed manner. This means that data have rarely been leveraged to the fullest potential. While data types such as controls, transactional information, complaints and losses have been used to mitigate risks emerging in day-to-day operations, overworked teams have not been able to prioritize combined analytics to reveal broader trends and patterns.
In recent years, regulatory authorities have developed greater capabilities to identify patterns in data. They have placed increased demands on large financial institutions to bring analytical rigor into their daily operational risk management (ORM) and compliance frameworks.
This new environment forced our client to increase its focus on combined analytics. They needed to more broadly leverage data patterns and trends to help mitigate, prevent and predict risks.
An in-house team was put in place by the bank to address these analytics needs. However, finding the right profiles with a balance between business acumen and technical savvy proved challenging.
We assembled a multi-disciplinary team with expertise in wealth management products and related risks, and experience in using a range of analytics tools. This type of tight partnership between business and technical experts has proven highly effective for demanding modern analytics, especially when combined analytics are required.
This team leverages industry standard tools like SAS and emerging tools like R to help proactively identify and manage bad actor risk, minimize internal losses, and support strategic operational risk decisions with data-driven insights. Projects have included leveraging advanced transactional data mining to identify bad actor activities and using string algorithms to improve name screening for AML monitoring.
Setting up of a dedicated team of risk and financial services experts and experienced analysts
Deployment of necessary industry standard and emerging tools
Sharing of insights into the full range of operational and compliance risks
Our dedicated team provided access to resources and expertise that had proven difficult to find locally. The client is extremely happy with our experts’ performance, noting faster response times to regulatory requirements and MRAs and a reduced number of high-profile settlements thanks to the identification of high-risk products.
Beyond the strong knowledge of data analytics tools and techniques, the team provides thought leadership and strong communication skills when tackling business problems, acting as true ‘translators’ of data into actionable insights.
Faster response to regulatory requirements and MRAs
Lower high-profile settlements
New methods for name screening for AML monitoring