In today’s world, financial crime is a major challenge for businesses, banks, financial institutions like fintechs and neobanks, and even governments. To combat this, a data-driven approach is needed to help identify suspicious activities and prevent them before they occur.
There is a lot of information that can help us figure out what is normal and what isn't when it comes to transactions. This information is part of the huge amount of data that is generated every day by both financial institutions and government agencies.
Having access to this data and being able to tell the difference between them is important for making technology that can accurately spot patterns of financial crime.
In this blog post, we will explore how a data-driven approach can help in the fight against financial crime, including the use of machine learning, analytics, and other technologies. We will also discuss using Flagright’s approach in order to create a secure environment for businesses and individuals.
Financial crime and the need for a data-driven approach
According to the FBI's Financial Crimes Report, the total losses due to financial crimes reported in 2020 was over $6 billion.
It is also estimated that financial crime is responsible for approximately 5% of the world's GDP being laundered annually. Every year, millions of people fall victim to financial crimes in the form of identity theft and other schemes.
Financial crime refers to crimes committed by an individual or a group of individuals involving the theft of money or other property belonging to someone else in order to obtain a financial or professional gain.
Financial crime can range from simple theft or fraud committed by a single person to large-scale, global schemes orchestrated by organized criminal syndicates. Financial crime is generally acknowledged to include the following offenses:
- Money laundering
- Terrorist financing
- Bribery and corruption
- Insider trading
Financial crime is an increasingly prevalent issue in today’s world and combating it requires a sophisticated data-driven approach. This approach involves using technology such as machine learning and analytics to detect suspicious activities, monitor transactions, and identify potentially fraudulent activity.
Additionally, this strategy also provides organizations with a variety of benefits, including improved security, compliance, and transparency. It is essential for businesses to understand the complexities of financial crime and have a data-driven strategy in place if they wish to protect themselves against it.
By utilizing advanced technologies such as machine learning and analytics, businesses can create a secure environment while still being able to operate with transparency and compliance.
From filing papers to artificial intelligence
Historically, financial institutions relied heavily on manual, human intervention in the regulatory reporting process; literally, humans put pen to paper. This is still common practice, especially in the case management workflow. Before suspicious activity and other compliance obligations are reported to regulators, several levels of case investigators physically review details and evaluate documents.
However, with massive amounts of data flowing in and out of financial institutions, individuals are unable to keep up with demand. Risk alert backlogs frequently grow faster than operations teams can handle.
Artificial intelligence, machine learning, natural language processing, and cognitive automation can be used to accelerate or automate a significant portion of labor-intensive work, lowering operational costs and freeing up people to focus on preventative interventions.
In addition to reducing operational workloads in case management, compliance teams are leveraging advanced analytics in a variety of preventative financial crime use cases, such as monitoring transactions in real-time, enriching the KYC process, improving sanctions screening performance, and assisting in the proactive identification of risks and opportunities.
A data-driven and intelligence-led approach to combating financial crime
It is clear that financial institutions are being challenged both internally and externally in order to meet the tedious demands of mitigating financial crime risks.
To align operational effectiveness with these demands, financial institutions need to look for creative solutions to issues such as transaction monitoring, false positive rates, KYC due diligence, and screening alert management.
Financial institutions are increasingly willing to go beyond simply flagging suspicious activity for compliance purposes. The goal is to use data and technology to identify potential criminal behavior more cost-effectively and prevent criminal activity from occurring in the first place.
Complete and accurate data is required to resolve these issues, and an improvement in data quality will have an immediate impact on the performance of existing monitoring and screening engines.
AI, machine learning, and automation, among other advanced analytics and cognitive techniques, can help filter out false positives and improve inefficiencies in existing investigative processes. Data and analytics have the potential to not only drive efficiencies and operational cost reductions, but also identify intelligence-led and data-driven ways to combat financial crime.
No financial institution is immune to the threat of financial crime or regulatory challenges, as criminals are growing more sophisticated and looking for new weaknesses in financial networks.
The good news is that cutting-edge AML compliance platforms, like Flagright, considerably outperform traditional solutions. With solutions like real-time transaction monitoring, to help find suspicious activity for in-flight transactions and post-monitoring use cases.
In addition to dynamic risk scoring, KYC/KYB orchestration, sanctions screening, and blockchain analytics, our comprehensive suite of products is designed to help your operation become more secure, efficient, and compliant.
Contact us here to schedule a free demo.