Financial Crime Data Analytics in Tunisia: A Growing Need for Innovation

The Challenge of Financial Crime in Tunisia

Tunisia’s financial services industry is facing a growing challenge in combating financial crime. With increasingly rigorous compliance requirements and vast amounts of data to process, banks are struggling to stay ahead of the game.

• According to a recent survey, the cost of global financial crime activity exceeds $1.3 trillion annually.
• Over $26 billion in fines imposed by regulators in the last decade alone highlights the need for change and innovation in the way financial services firms approach regulatory compliance.

The Benefits of Innovation

Banks that innovate and adopt new technologies and techniques will be industry leaders in the years to come. From traditional manual processes to advanced data and analytics techniques, there are opportunities for banks to improve their ability to detect and prevent financial crime.

• Advanced data and analytics techniques such as artificial intelligence, machine learning, natural language processing, and cognitive automation can be used to accelerate or automate labor-intensive work.
• This allows staff to focus on preventative interventions, reducing operational costs and improving overall efficiency.

Preventative Financial Crime Use Cases in Tunisia

In Tunisia, financial institutions are leveraging advanced analytics in a range of preventative financial crime use cases, including:

Enriching the Know Your Customer (KYC) Process

Augmenting human activity with machine learning techniques can achieve a more holistic view of the customer, enhance data used for due diligence, and provide a contextual basis for determining customer risk.

Enhancing Sanctions Screening Performance

Emerging AI and analytical methods can be used to address operational efficiency issues related to case investigation, substantially lowering the number of alerts to be safely dispositioned.

Examples of Innovative Solutions

Examples of innovative solutions to age-old problems include:

• Transaction Monitoring: Machine learning models can enrich transaction monitoring alerts and boost Suspicious Matter Report (SMR) conversion rates, predicting AML scenarios before they occur.
• Know Your Customer (KYC): Augmenting human activity with machine learning techniques can achieve a more holistic view of the customer, enhance data used for due diligence, and provide a contextual basis for determining customer risk.
• Sanctions Screening: Emerging AI and analytical methods can be used to address operational efficiency issues related to case investigation, substantially lowering the number of alerts to be safely dispositioned.

Conclusion

To align operational effectiveness with the demands of mitigating financial crime risks, organizations must seek innovative ways to address issues surrounding SMR conversion rates, KYC due diligence, and screening alert management. Complete and accurate data is essential to resolving these issues, and an uplift of data quality will have immediate effects on the performance of existing monitoring and screening engines.

By leveraging advanced analytics and cognitive techniques, Tunisian banks can improve their ability to detect and prevent financial crime, ultimately driving growth and success in the region.