Compliance costs in mortgage banking domain are rising, in line with continuing exponential growth in the volume, pace and variety of regulation. Most of the mortgage lenders still rely on highly manual processes and age-old systems to gather regulatory data and manage regulatory change. Adopting technologies in AI can help mortgage banks in solving these complex regulatory compliance challenges.
Regulatory compliance challenges in mortgage industry
- Continuous regulatory changes and rising repurchase risks.
- Tracking the regulatory changes and assessment of its impacts.
- Information governance.
- Technology risk and cyber security.
- Lack of integration between data and document systems.
- Lack of workflow solution for process changes within the mortgage bank.
- Manual QC processes and audits.
Issues with the traditional approach for regulatory change management
- Decentralized system of regulatory change management carries the risk of missing out on key regulatory changes and updates.
- Classification and impact assessment of regulatory changes is manual and human intensive and therefore drives up compliance costs.
- Due to the complexity and volume of regulatory changes, these manual processes are time consuming and there is high rate of work duplication.
- Moreover there is no single view of the impact of the regulatory changes on the workflows within the organization.
- Since there is a lack of workflow solution for process changes within the mortgage bank, frequent regulatory updates becomes a constant cause of concern for the lenders.
AI processes for solving regulatory compliance challenges
- Automated crawling or collection of regulatory content in real time into a single source of feed leveraging web crawling technologies that can monitor vast amounts of URLs and webpages with regulatory information.
- Implementing Intelligent Data Capture tools to extract data fields from borrower and seller closing disclosures. These data are then automatically compared with loan estimates and process statements to check for data mismatches and execute to tolerance rules to identify any data fields which are out of compliance.
- Automated placing, classification and structuring of the regulatory data into databases like NoSQL or graph. This content which is coming in many formats is processed so that a single structured schema is available.
- Technologies like Semantic web and Natural Language processing (NLP) are used to process this regulatory information against regulatory ontologies to map with regulatory standards.
- Digital data retrieval systems using technologies like Optical Character Recognition (OCR), Intelligent Content Recognition software should be implemented to eliminate the data errors at the source.
- The policies, processes, controls and organizational hierarchies of the mortgage bank are taken into account to automate the mapping of the regulatory statements onto the internal infrastructure and compliance terminologies that the mortgage bank leverages.
- Therefore the assessment and analysis of regulatory statements on policies and controls will be fully automated based on NLP and ontological mapping so that compliance professional can perform decision making and process changes based on these insights.
- Instant notifications of regulatory changes that create compliance gaps can be automated to enable timely remediation.
- Automate Uniform Closing Dataset (UCD) preparation and delivery to Fannie Mae and Freddie Mac without manual intervention.
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