
Financial institutions continue to face compliance challenges due to increased regulations, growing businesses and ongoing inefficiency mitigation. Anti-money laundering (AML) and sanctions compliance demand quick and proactive adaptation of processes in order to stay ahead of the curve. Chief challenges for financial institutions include navigating highly manual tasks such as alert reviews and clearance and paper instrument transaction processing.
Many tools have been developed in an effort to mitigate inefficiencies and avoid potential billions in fines for violations of US economic sanctions. Solutions such as optical character recognition (OCR), native language processing (NLP), robotic process automation (RPA), AI and machine learning are at the forefront of technologies in transaction processing efficiency, risk mitigation and savings in overall workforce costs. However, the complexity and sophistication of these tools require correct implementation.
Traditional methods of screening simply cannot keep pace with modern business demands. The manual nature of processing paper instruments paired with the sheer volume of transactions creates limitless potential for error because data extraction and analysis, screening alert review and record keeping are complex. Current challenges include:
Data extraction and analysis
Screening and alert review
Quality assurance and audit
Despite increasing regulatory scrutiny and sanctions risk exposure, many financial institutions are still using outdated technologies and inefficient manual processes that result in inaccurate results. In order to remediate these inefficiencies, financial institutions should integrate automated solutions into their current technology infrastructure.
Financial institutions that adopt new, automated technologies can reap a number of benefits, including:
Optical character recognition (OCR)
OCR is a process that electronically converts text by singling out specific characters from a scan or an image of a paper instrument. The extracted characters are then rebuilt into specified fields within the end system. OCR can be designed to accommodate different formats of text (i.e., handwritten or printed) and adapt to different scripts.
Text analytics and natural language processing (NLP)
Advanced text analytics and NLP are artificial intelligence techniques that can be used to discover relevant information in text and transform it into data. In the context of paper screening, this technique can be used to replace manual paper instrument analysis and extraction procedures (e.g., classifying and selecting names and places from documents).
Integrating new technologies for paper instruments into an existing sanctions screening program is becoming increasingly necessary for financial institutions facing rising costs associated with compliance and demand for enhanced controls. As financial institutions continue to deal with higher transaction volumes and shorter processing times, it is important to understand and consider how new solutions can positively impact and reduce the risk of misaligned paper screening processes.
When it comes to innovating and automating screening processes, financial institutions will need to determine which automation opportunities are most pertinent, given their current processes, operations and risk appetite.
Our Financial Crimes Unit (FCU) brings together the full breadth of PwC’s technology, regulatory and investigative experience with the work of over 2,000 global financial crimes professionals in cybersecurity, anti-money laundering, sanctions, fraud, anti-bribery and anti-corruption. We bring an adaptive, comprehensive approach that reflects that of major financial institutions and government agencies. With secure systems, future-proof technology and compliant operations, you’ll find a path forward where all else are hindered by obstacles.