The Future of Fighting Financial Crime

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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.

Top Four Digital Trends for the Next Five Years

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The sheer amount of disruptions the banking industry endured in 2020 has cast a new light on banking industry trends. But will these disruptions translate into major shifts or further acceleration — especially with regard to digital growth — over the next five years?

Last year, banks saw an unprecedented influx of deposits — $2.4 trillion, according to the Federal Deposit Insurance Corp., with gains going primarily to the biggest banks. Looking ahead, we predict further ascendance of the moneycenter banks, but still see opportunities for smaller, nimbler banks to remain competitive when it comes to digital banking innovation. 

Disruptions and Opportunities
The Covid-19 pandemic demonstrated compelling reasons for community banks to step up their digital banking efforts. In-person interactions are limited, and even in places where banks are open, many customers may not feel safe. The preference for remote banking is likely to continue into the future: Qualtrics XM Institute found that 80% of people who start banking online are at least somewhat likely to continue.

But the coronavirus is just another tick in the column in favor of greater investments in digital banking. Many community banks have already rolled out online service options in the past few years. Their efforts and investments to make digital banking more user-friendly and efficient is paying dividends.

For instance, Cross River Bank, a community bank with $11.5 billion in assets in Fort Lee, New Jersey, emerged as one of the top Paycheck Protection Program lenders while simultaneously gathering $250 million in deposits in just 15 days. As innovative banking technology becomes more readily available, community banks will have convenient alternatives to legacy vendors that don’t require a massive budget.

What’s Next in Digital Banking?
Banking will continue to evolve rapidly over the next five years. In particular, community institutions should take heed of four trends.

1. Hyper-localized products will help community banks compete with larger institutions.
Community institutions should focus on overall product offerings, not just rates. Digital solutions can offer better tools to connect with the local community, as well as expand a bank’s customer base nationwide.

A major trend for banks to consider is verticalized banking. The big banks aren’t capable of delivering hyper-localized or targeted offerings to the same extent. While these services already exist for certain demographics, such as military personnel and students, we’re seeing this expand to female entrepreneurs, minority-owned businesses and tech developers.

2. Banks are leveraging technology to deepen community relationships.
Covid-19 relief efforts created an opening for tech-savvy community banks to win market share and goodwill among small businesses and communities at-large. These relief efforts will likely continue to be a major area for investment and innovation over the next few years.

A prime example of this is Quontic Bank’s #BetheDrawbridge campaign. The Astoria, New York-based bank’s Drawbridge Savings account matches a portion of interest paid to account holders into a fund providing financial relief to New York City families and businesses. Not only is the bank leveraging digital account opening to broaden its footprint, but also building goodwill within its home-base. 

3. Real-time transaction monitoring becomes table stakes to compete online.
While the U.S. has been slow to adopt real-time payments (RTP), the time is near. The Federal Reserve is working to release its RTP network, FedNow, by 2024; The Clearing House’s RTP Network is quickly expanding.

Community banks should prepare for real-time banking — not only through the implementation of real-time digital servicing, but also through real-time transaction monitoring. Money moves today; if banks don’t receive a report until the next morning, it’s too late. As real-time payments become more accessible, real-time transaction monitoring will be table stakes in order to prevent fraud, mitigate costs and stay competitive.

4. The business banking experience will see major growth and user-friendly improvements.
Commercial banking has so far lagged behind consumer services, remaining manual and paper-based. Fortunately, the innovations that have emerged in personal banking are migrating to the commercial space. This will likely become a major area of focus for technology firms and financial institutions alike.

Looking Ahead
In the next five years, smaller banks will need to double down on digital banking trends and investments, taking advantage of their nimble capabilities. The right tools can make all the difference — the best way for banks to fast-track digital offerings in the next stage of their evolution is to find the right partners and products for their needs.