Creating a Real ‘Data-driven’ AML Program That Works
In a recent webinar, our Analytics and Data Science (ADS) Consulting team addressed how to create an effective data-driven anti-money laundering (AML) compliance program. If you missed it or are just looking for a quick refresher on what was covered, here is a recap of what was discussed.
The analytics opportunity
To fight financial crimes, organizations today are increasingly focused on data-driven analytics, as evidenced by PwC’s Global Economic Crime and Fraud Survey 2018. The survey found that 42% of companies have increased spending on combating financial crime over the past two years, up 2% from 2016, and that 44% intend to boost their spending over the next two years.
Although businesses are spending more on technology, a 2016 global AML survey from Dow Jones and ACAMS indicates that decision makers cite data accuracy as the greatest factor impacting their AML compliance programs. Through 2016, 60% of respondents also raised concerns about the shortage of skilled staff and outdated technology.
These challenges present opportunities for improving and leveraging technology to improve processes, including eliminating many manual processes, equipping staff with information to make informed decisions and reducing the cost of compliance. Technology also allows institutions to automate control validation to help meet regulatory requirements, validate reports and ensure that controls are vetted properly.
In addition, technology can help compliance departments fill knowledge and skills gaps as well as quickly adapt to changes in environment, technology, organization and services, which provides a holistic view of your compliance program and your data.
Employees and departments to involve
It’s not just the compliance department that needs to be involved in building an AML program. Members of Financial Risk Management, Internal Audit Management, IT Risk and Compliance, Operational Risk Management should also provide input. In more progressive organizations, increasingly CIOs, chief marketing officers and chief data officers are becoming involved.
General Counsel is also needed since many regulations require interpretations or are evolving to meet growing threats. Finally, process owners and CIOs also need to be involved because they will put the processes in motion.
Data and processes involved
There is an increasing need to gather and cross-reference information across the enterprise, rather than in slices. Advances in technology help with this process, and enable an integrated approach, which allows multiple teams to come together and focus on critical risks.
Once an organization is able to leverage data from various enterprise systems, AI, machine learning, statistical reasoning, rules-based analytics help generate more sophisticated insights for compliance teams. These can then be used to start conversations around the prevention of financial crime and stopping people from doing what they shouldn’t be.
Executing for your business
When looking at the effectiveness of your AML program, you must first identify the current state of your activities, roles and responsibilities, and system capabilities. Many organizations lack up-to-date documentation on their policies, procedures and processes.
When we work with organizations, we like to assess the processes and identify the pain points within, then identify risk, appropriate analytics, and opportunities to automate processes. Being able to generate analytics to monitor the effectiveness of controls is essential, and having the technology to do so makes compliance easier, especially for small teams.
Want to see the full recording of the webinar for more information? Find it here.
About Anu Sood
Anu Sood (LinkedIn | Twitter) is the Director Marketing at CaseWare RCM and is responsible for the company’s global marketing strategy. She has over 20 years of experience in product development, product management, product marketing, corporate communications, demand generation, content marketing and strategic marketing in high-tech industries.