Category: Financial Institutions and Banking

Abstract:   As federal banking regulators intensify their scrutiny of Bank Secrecy Act and Anti-Money Laundering compliance, community banks need to become more proactive in combating money laundering. One potential tool worth considering is data visualization software. This article examines recent compliance requirements and how to effectively incorporate data visualization software into a bank’s antifraud lines of defense.

Data visualization helps banks combat money laundering

 

As federal banking regulators intensify their scrutiny of Bank Secrecy Act and Anti-Money Laundering compliance, community banks need to become more proactive in combating money laundering. One potential tool worth considering is data visualization software.

Increased emphasis on BSA/AML

Several recent developments reflect the federal banking agencies’ increasing concern about Bank Secrecy Act and Anti-Money Laundering (BSA/AML) compliance efforts:

• In July, the Financial Crimes Enforcement Network (FinCEN) introduced new customer due diligence (CDD) rules that require institutions to incorporate beneficial ownership identification requirements into existing CDD policies and procedures.
• In its Spring 2016 Semiannual Risk Perspective, the Office of the Comptroller of the Currency (OCC) alerted banks to increasing BSA/AML risks associated with technological developments and new product offerings in the banking industry.
• In recent months, regulators have been scrutinizing automated monitoring systems used by banks to detect suspicious activity to ensure that they’re configured properly.

And don’t assume that regulators are limiting their heightened scrutiny to larger banks. The OCC’s report noted that some large banks are restricting certain customers’ activities or closing their accounts because of BSA/AML concerns. Displacement of these customers, the report warned, “may result in higher-risk customers moving to smaller and less sophisticated banks . . . that potentially have less experience managing the associated BSA/AML risks.”

Banks that fail to take reasonable steps to detect and prevent money laundering activity risk not only government fines, but negative publicity and reputational risk.

Seeing the big picture

Data visualization software — also known as visual analytics — can be a powerful AML tool. Traditional AML software products and methods do a good job of detecting known AML issues. But data visualization software, which is commonly used as an antifraud weapon, excels at spotting new or unknown AML activity.

As criminal activity becomes more sophisticated and more difficult to detect, traditional AML software or methods may no longer be enough. Data visualization software creates visual representations of data. These representations may take many different forms, from pie charts and bar graphs to scatterplots, decision trees and geospatial maps. Visualization helps banks identify suspicious patterns, relationships, trends or anomalies that are difficult to spot using traditional tools alone. It’s particularly useful in identifying new or emerging risks before they do lasting damage.

Criminal enterprises that wish to launder money typically use multiple entities and multiple bank accounts, both domestic and foreign. Using data visualization software, banks can map out the flow of funds across various accounts, identifying relationships between accounts and the entities associated with them. Data visualization can reveal clusters of interrelated entities that would be difficult and time-consuming to spot using traditional methods.

These clusters or other relationships don’t necessarily indicate criminal activity. But they help focus a bank’s AML efforts by pinpointing suspicious activities that warrant further investigation.

Get your data in order

Perhaps the biggest challenge in taking advantage of data visualization software and other automated AML tools is the fact that, at many institutions, information is scattered among many separate systems. For data visualization to do its job, the first step is to collect and integrate this information into a single database. Once this is done, data visualization software can help your bank detect potential AML issues more quickly and effectively.

© 2016

Abstract:   In a dramatic change to bank accounting guidelines, the FASB recently finalized its long-awaited CECL model for estimating credit losses. This article highlights the most important elements of the new model, including its forward-looking approach (which involves its treatment of covered and PCD assets, as well as its position on estimating losses and accounting for AFS securities) and its impact on community banks. A sidebar explains when banks must adopt the CECL model.

Accounting for credit losses

Get ready for CECL

In a dramatic change to bank accounting guidelines, the Financial Accounting Standards Board (FASB) recently finalized its long-awaited Current Expected Credit Loss (CECL) model for estimating credit losses. The new standard — Accounting Standards Update (ASU) No. 2016-13 — applies to all organizations. But financial institutions will be affected the most.

Although CECL’s impact will depend on a particular institution’s facts and circumstances, it will cause many banks to increase their allowances for loan and lease losses (ALLL), affecting both earnings and capital.

Forward-looking approach

Currently, banks measure credit impairment based on incurred losses. Under CECL, they’ll adopt a forward-looking approach, recognizing an immediate allowance for all expected credit losses over the asset’s life.

The FASB believes that the incurred-loss model, which delays recognition of credit losses until they become probable, provides information that’s “too little, too late.” CECL addresses this problem by requiring organizations to record credit losses that are expected, but don’t yet meet the “probable” threshold. It also sets a single impairment model for all financial assets carried at amortized cost, in contrast to the multiple models used today.

Here are some highlights of the new standard, which doesn’t take effect for several years (see “When must you adopt CECL?”):

Covered assets. CECL will apply to 1) financial assets measured at amortized cost, including loans, held-to-maturity debt securities, trade and reinsurance receivables and net investments in leases, and 2) certain off-balance-sheet credit exposures, such as loan commitments and financial guarantees.

Estimating losses. The allowance for credit losses will be the difference between financial assets’ amortized cost basis and the net amount expected to be collected. To estimate expected losses, banks will consider a broader range of data than they do under current standards, including not only historical and current information, but also “reasonable and supportable forecasts that affect the collectability of the reported amount.”

Potential impact. Some experts, including the Comptroller of the Currency, predict that CECL will increase banks’ loan loss reserves by 30% to 50%. Other estimates are lower, but ultimately the impact on a particular institution will depend on a variety of factors, including historical experience, current conditions and market forecasts.

Accounting for AFS securities. The new standard will change the way credit losses are measured for available-for-sale (AFS) debt securities, requiring banks to use an allowance for credit losses. Unlike the current practice of writing down individual securities for other-than-temporary impairment, the new approach will allow banks to recognize subsequent reversals in credit loss estimates in current income. In addition, the credit losses on AFS debt securities will be limited to the amount by which fair value falls short of amortized cost.

Treatment of PCD assets. To simplify the accounting for purchased credit-deteriorated (PCD) assets, the ASU requires institutions to recognize an initial allowance for credit losses. Thereafter, such assets will be treated similarly to other financial assets measured at amortized cost.

Impact on community banks

In the years after CECL was first proposed, many community banks expressed concern about its potential complexity and the need to implement sophisticated modeling techniques. A recent joint statement by federal banking agencies should help ease these concerns. According to the statement, CECL will be scalable to institutions of all sizes. And it doesn’t prescribe specific estimation methods — rather, institutions should apply judgment in developing methods that are appropriate and practical.

The agencies “do not expect smaller and less complex institutions will need to implement complex modeling techniques.” Rather, they expect that these institutions will be able to meet CECL’s requirements by building on existing systems and methods for estimating credit losses. For example, a bank that uses historical loss rate methods would need to adjust its inputs to estimate remaining lifetime credit losses.

The statement also points out that CECL contemplates pooling assets with similar risk characteristics when estimating expected credit losses. In most cases, smaller banks will be able to continue using established practices for segmenting their portfolios.

Be prepared

CECL’s effective date is several years away. Nevertheless, banks should begin preparing soon to develop institution-appropriate credit loss models, evaluate the potential impact on capital, and identify any necessary system changes or additional data collection requirements.

 

 

Sidebar: When must you adopt CECL?

Here’s a summary of the new standard’s effective dates:

Organization type	Takes effect for:	Interim periods affected
SEC filers	Fiscal years beginning after 12/15/19	In 2020
Other PBEs* (non-SEC filers)	Fiscal years beginning after 12/15/20	In 2021
Private companies	Fiscal years beginning after 12/15/20	Beginning after 12/15/21

*Public business entities

Early application is permitted by all entities for fiscal years beginning after December 15, 2018, including interim periods within those fiscal years. For loans and other financial assets carried at amortized cost, banks will recognize a cumulative-effect adjustment on their balance sheets as of the beginning of the first reporting period in which CECL is effective.

© 2016