The Future of Fighting Financial Crime

The anti-financial crime landscape is continuously evolving, and financial institutions need to stay a step ahead of emerging fraud trends and regulatory compliance challenges to protect their customers and themselves from loss and reputational damage.

As consumers become increasingly reliant on the speed and convenience of digital banking products, institutions should consider end-to-end financial crime management solutions that offer real-time fraud detection, targeted AML transaction monitoring and automated regulatory reporting to fight financial crime and strengthen compliance. With artificial intelligence, including machine learning and robotic process automation (RPA), behavior-based anti-financial crime management solutions can help institutions increase the effectiveness and efficiency of their fraud and AML programs.

But artificial intelligence relies on the power of big data. Anti-financial crime management solutions need an immense data set from multiple sources, including core, ancillary, open-source, third-party and consortium data. Artificial intelligence can be applied to this data with cross-institutional analysis in a cloud-based environment. Solutions built with big data and artificial intelligence reduce false positives and increase the quality and accuracy of alerts.

Analytical agents built with machine learning algorithms continuously analyze data to improve analytical performance. A large, cross-institutional data set in a cloud-based environment allows machine learning agents to train on labeled data from thousands of institutions, achieving performance levels that cannot be matched by a single institution with a limited, isolated and restricted data set. Machine learning can significantly improve analytical performance, helping institutions reduce false positives and reduce the alert review time to increase the efficiency of investigations, while continuing to detect new and emerging criminal trends.

Machine learning agents use mathematical and statistical models to learn from data without being explicitly programmed. These agents analyze new data, including transactions, demographics and customer behavior, and utilize this evidence in transaction monitoring alerts. The alerts provide feedback to the cloud-based data set, where the training data, which includes the transaction, demographic and customer-behavior evidence, and labeled data, such as cases, marked transactions and return items, are input to a machine learning algorithm.

This data is used to train many different types of machine learning agents to determine which type of agent performs best for a particular typology. Before training an agent, the data is split into training, testing and validation data sets, so that the results of the training can be validated in an unbiased manner. Anti-financial crime management solutions can use precision, recall and false positive rate to validate analytical performance and assign the most suitable analytical agent to a particular fraud or money laundering typology.

Strengthening Processes with Robotic Process Automation

Institutions can leverage technology such as RPA to improve internal processes, strengthen anti-financial crime management programs and ensure regulatory compliance. Using RPA to improve workflow automation can save financial crime investigators time by reducing manual tasks, automating steps in alert triage and regulatory reporting processes, and through prepopulating and submitting reports with consistency, speed and accuracy.

Enhanced data collection through RPA reduces human error that occurs during manual data collection and transference. It also automatically and reliably integrates multiple data sources in your anti-financial crime management and compliance solutions.

Anti-financial crime management programs can use RPA agents to validate information populated in a currency transaction report or a suspicious activity report, automatically submit reports, intelligently package related alerts together, and automatically assign work to a team or an investigator. Such solutions can also automatically triage alerts and segment customers into appropriate risk categories, increasing the efficiency and effectiveness of financial crime investigations.

To stay ahead of financial crime trends, financial institutions should consider the benefits of cloud-based solutions that leverage artificial intelligence to increase the effectiveness and efficiency of anti-financial crime management and compliance programs.

Don’t Forget Your Umbrella: How to Protect Your Bank From Financial Crimes

risk-management-6-13-17.pngWith banks of all sizes facing significant challenges in the management of financial crime risk, senior management and bank board members need an unambiguous understanding of the strengths and weaknesses of their organizations’ financial crime compliance strategy.

The escalation of mobile banking, the burgeoning role of fintech in banking and the spread of cybercrime are only a few of the key reasons for banks to establish a process that views financial crime risks in the aggregate—under one umbrella. Further, in our view, directors must have a firm grasp with respect to how the program has been designed and implemented.

An integrated view of financial crime compliance risk can give board members a sense of confidence that management has a robust financial crime compliance program in place. A view of issues in the aggregate provides management the ability to understand the entirety of the financial crimes landscape at their firm.

At their core, these programs require a dynamic and agile mindset at the board level. Directors must possess a level of confidence that management has established a strategic, well considered approach to detecting, preventing and reporting financial crime. A carefully managed, well designed, and integrated plan can also create considerable governance benefits across internal silos.

For banks currently without an integrated plan, the creation of such a plan requires:

  • A strategic vision of a future program that engages senior management in the first line of defense (lines of businesses and operations) in the design of the vision—and has buy-in by the entire board.
  • The integration of teams that in the past have approached such risks in a separate manner, such as compliance programs for anti-money laundering, anti-bribery and corruption, and Office of Foreign Assets Controls.
  • A vision for how to change or enhance the bank’s information technology (IT) infrastructure.
  • The designation of an individual as the bank’s financial crimes compliance officer.

Building an integrated financial crimes program under an umbrella structure presents opportunities for collaboration, improved data aggregation and analytics capabilities, heightened board awareness of the bank’s control environment, and the possibility of cost savings and enhanced regulatory compliance.

The establishment of a centralized financial crimes compliance unit, however, requires a multi-faceted approach. Employee roles and responsibilities will likely shift, policies and procedures many need to be consolidated to reflect the new approach, and compliance reporting mechanisms and IT responsibilities will be altered.

Recognizing that the landscape will shift, we offer a roadmap to an integrated financial crimes compliance program. Here’s a synopsis of our five-step plan for your board’s consideration:

  1. Compliance leaders recognize the importance of cultivating partnerships with business-unit leaders across the bank—as well as their internal audit teams. Thus, building a cross-functional working team is a must across the bank’s “three lines of defense:” the front office and lines of business, the support functions such as compliance and finally, audit. These members should consider perceived benefits, anticipated costs and potential obstacles. Dialogue and trust is essential.
  2. The team should strive to gain a clear view of the bank’s current risk management efforts and assess the underlying financial crimes risks. Too many institutions stumble at this stage by adopting models that may work for larger or more-regulated institutions, or conversely for smaller institutions with a different product mix or jurisdictional presence.
  3. The cross-functional team should draft a working plan for the centralized compliance unit, and the team should provide the draft plan, which would include the recommended step-by-step approach to establishing the unit, to board members and executive leadership for review. The plan would identify the individuals who will design and roll out the changes, the governance and oversight structure of the transformation program, and the unit’s staffing model.
  4. Perhaps as much as any these steps, clear and frequent communication to bank personnel about the program’s intentions, benefits and impacts is vital. Board members should be satisfied that management has established a plan for the timing and cadence of communications, has identified which audience will be targeted at each step, and has created specific messages to the bank staff regarding why the establishment of the unit is necessary and how it will benefit the organization.
  5. Once the bank has embedded its Financial Crimes Compliance Program, management must be certain that monitoring and testing mechanisms are working continuously, and that the firm is equipped to deal with changes as regulations change or are introduced.

A final reminder is worth noting: The journey is never over. Financial crime compliance risk, as a board agenda item, should be a constant.