Location intelligence – or technology focused on the collection, analysis and visualisation of geographic data – has a variety of uses across different industries. For instance, this technology can be used by law enforcement to track criminal activity, or by retail companies to monitor customer behaviour. While some financial institutions use geospatial tools to gather customer data and manage risk, this technology hasn’t been widely adopted due to concerns of regulatory authorities regarding aspects of its functionality and credibility. If integrated properly, location intelligence can enhance anti money laundering (AML) practices, bolster financial inclusion and refine fair lending compliance in financial services.
Anti money laundering
Location intelligence can improve AML practices by enhancing customer due diligence (CDD) and increasing the informational value of suspicious activity reports (SARs).
Financial institutions, as part of the customer onboarding process, are required to administer CDD programmes that evaluate customer risk profiles. These assessments consider, among other things, geographic risk associated with each customer account. For AML purposes, a customer’s risk profile tends to increase with the volume of transactions occurring in “high-risk” locations associated with money laundering.
To curtail money laundering risks, institutions engage in a practice known as geographic ‘de-risking’, whereby firms avoid or terminate customer relationships due to high geographic risk. Geospatial technology, however, mitigates the need for widespread de-risking, as firms can use real-time location data to pinpoint high-risk locations and selectively preserve lower-risk customer relationships.
Firms can use location intelligence to geocode transactional data to build maps that highlight money laundering activities. These maps highlight relationships and patterns across different locations to identify high-risk customers in a manner that better corresponds with actual geographic risks. The precision of these maps can be honed further by sharing data across financial institutions.
Section 314 (b) of the USA Patriot Act provides a safe harbour that permits financial institutions to share information with other 314 (b) firms to identify and report money laundering activities. Information shared includes geospatial data, which would collectively improve the detection and reporting of money laundering activities across the financial system.
Location intelligence also bolsters the informational value of SARs by lowering the volume and increasing the data quality of SARs filed with regulatory authorities. Under the current framework, institutions annually submit more than one million SARs. These SARs, however, tend to be of little pragmatic value in identifying money laundering offences, as they seek merely to comply with regulatory protocols.
Geocoding SAR data at the institution level will provide more accurate, granular detail of suspicious activity. Firms and law enforcement officials can use this data to conduct proximity searches related to specific locations, and search for SARs containing similar characteristics. This additional layer of data will increase the value of SARs and better support law enforcement efforts in detecting, preventing and prosecuting money laundering.
Location intelligence can bolster an institution’s Community Reinvestment Act (CRA) rating by improving its geographic distribution of loans to underbanked and low- and moderate-income (LMI) communities.
CRA regulations promote financial inclusion by requiring institutions to map geographic assessment areas in which they operate. Assessment areas generally include branch and office locations, and consist of different metropolitan statistical areas (MSAs), metropolitan divisions, or political subdivisions where an institution maintains a substantial part of its loans. Assessment areas are a focal point for CRA compliance as regulators issue CRA ratings based, in-part, on a firm’s success in meeting the credit needs of LMI individuals in its assessment areas.
Institutions can use location intelligence to map demographic information and more efficiently deliver targeted financial services to LMI individuals. This technology combines a firm’s data with census statistics to highlight LMI areas and predict demographic shifts affecting an institution’s lending operations. From this, an institution can, subject to fair lending considerations, adjust its credit programmes or modify its assessment areas to improve its distribution of loans to underbanked and LMI geographies. This approach will enable a firm to develop a CRA plan that closely aligns its lending objectives with the needs of LMI communities it serves.
Geocoding lending activities to serve LMI communities may also be viewed by regulators as demonstrative of a firm’s commitment to innovative and flexible lending practices. In evaluating lending performance under the CRA, regulators consider a firm’s use of innovative or flexible lending practices. These practices include new approaches to credit underwriting, such as augmenting traditional underwriting with geospatial data to benefit LMI geographies. Therefore, if implemented correctly, location intelligence can improve a firm’s CRA rating.
Fair lending compliance
Financial institutions can use geospatial mapping to assess regulatory compliance and prevent violations of fair lending laws and regulations.
The Equal Credit Opportunity Act (ECOA) and its implementing Regulation B prohibit creditors from discriminating against applicants in any aspect of a credit transaction based on race, gender, national origin and other protected characteristics. Two types of discrimination impact lenders:
- disparate treatment (acts, statements or practices leading to discrepancies in treatment)
- disparate impact (facially neutral policies that negatively and disproportionately impact a protected class even if no discriminatory intent is present).
While institutions generally understand it is illegal to treat borrowers differently due to race or national origin, fair lending issues often arise from an institution’s reliance on metrics that disproportionately impact a protected group. For example, a disparate impact may occur where a lender’s policies have the effect of discouraging persons with protected characteristics from applying for credit.
To prevent fair lending violations, institutions periodically test their loan portfolios. These tests, however, often fall short of identifying fair lending issues, because testing models lack key data regarding demographic trends and patterns, and such models don’t always consider the relationship of such data to an institution’s lending activity.
Geospatial tools enhance fair lending testing by allowing financial institutions to geocode loan data and map fair lending issues. This approach combines demographic data with different customer data sets to highlight geographic fair lending risks associated with a firm’s loan portfolios. Specifically, loans can be mapped across specified time periods based on default risk to identify pricing discrepancies, redlining and related discriminatory issues. This allows an institution to assess, in real-time, whether its lending activities, practices and policies violate ECOA and Regulation B. Institutions further benefit from integrating geospatial tools, as enforcement authorities also use maps in identifying and enforcing fair lending violations.
Today’s financial institutions retain a wealth of customer, transactional and related data. If analysed appropriately, such data can pre-emptively uncover financial crimes, promote inclusion and ensure fair lending compliance. Regulators should engage institutions and relevant stakeholders to implement geospatial solutions that will mutually benefit financial institutions and the regulatory community.
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