Machine Learning in Risk and Compliance

Businesses utilize countless resources to ensure regulatory compliance and reduce the effects of risk on business continuity. Artificial Intelligence can help businesses save millions each year in mitigating risk, maintaining traceability and accountability in their organizations.

COMPUTER VISION

Territory Supervision

Most businesses maintain a strict policy on who is allowed on their premise. Manual methods of supervision are prone to oversights and unauthorized personnel can cause asset and information risks in the workspace. Camera systems, backed with Computer Vision, can keep better track of staff, visitors and other entities in a secure workspace. AI can identify anomalies quickly and send automated alerts, saving the business millions in thefts, property damage, information breach among others.

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COMPUTER VISION

Automatic Number Plate Recognition

In many gated communities and secure premises, vehicle screening is a manual and mundane task. AI and Computer Vision can be used to automatically check if plate numbers belong to registered employees, residents, vendors, workers etc. and allow automatic entry. Office and resident complexes, schools, construction sites, manufacturing plants, loading docks, freight entrances use this AI implementation to secure their workspace against intruders.

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COMPUTER VISION

Drone Based Inspection

Organisations rely on availability of utilities around the clock for their daily activities. Monitoring the security of these assets is a costly and dangerous task. Businesses incur large sums in deploying helicopters for inspecting power grids, oil & gas pipelines, wind turbines, solar panels and other assets . Using drones fitted with cutting edge Object Detection technology, these assets can be monitored in a very cost effective manner. Inspection officers can assess the risk associated with these assets, such as component integrity, wiring issues, aging infrastructure, material corrosion, vegetation overgrowth etc.

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NATURAL LANGUAGE PROCESSING

Bank Transaction Screening

All bank transactions are screened to check if the entities involved are on a blacklist. Legacy filtering systems produce a high rate of false positives that then must be further escalated for review. Using Named Entity Recognition (NER), AI can identify entities that are parties to suspect transactions. Extracting party details from transactional text information, ML models can review transactions faster and with more accuracy. By implementing AI, banks can massively reduce regulatory costs due to non-compliance fines.

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