Next-gen financial crime methods need next-gen crime prevention
South Africa has the highest percentage of economic crime in the world. And half of the top 10 countries that reported economic crime in 2018 are in Africa. Despite increased efforts by financial service providers to combat fraud and money laundering, as well as intensified compliance pressure from regulators, the current approach to combating financial crime is ineffective.
The estimated annual cost of money laundering and associated crimes is between $1.4 trillion and $3.5 trillion, and global money laundering activity is estimated to be between 2% to 5% of global GDP. The figures are so high because only 10% of the suspicious transaction reports filed by financial services institutions lead to further investigation.
But an even bigger issue, says Lizette Sander, product manager at Bateleur Software, is that financial institutions are trying to prevent new risk with old, siloed technologies.
The state of financial crime presents an opportunity to merge fraud and money laundering activities, to get a holistic overview of suspicious behaviour across products and services, says Sander.
“By working in silos, organisations are missing an opportunity to optimise financial crime detection,” she says.
With AML and fraud departments working with their own solutions and data – and not communicating their findings – related criminal activity is more likely to slip under the radar. When viewed in silos, these activities seem “normal”, but when viewed holistically, criminal networks and hidden relationships start to emerge that otherwise would have gone undetected.
“By pooling resources, integrating tools and solutions, and improving collaboration, financial institutions will be more efficient in investigating financial crimes and identifying risks that might have been missed using a siloed approach. With more data available across departments, alerts are resolved quicker and decisions to investigate are more accurate.”
Criminals are adopting next-generation techniques and banks can no longer rely on yesterday’s approaches and solutions to fight back, she says. They need a next-generation approach of their own if they are to transform their compliance processes.
Old dog, new tricks
Financial institutions need to move away from rules-based-only compliance models and explore the capabilities of technologies like automation, advanced analytics, artificial intelligence (AI) and machine learning to gain a single view of their customers, says Sander.
“We need to start trusting digital technology to sort criminals from customers. It’s encouraging to see a big drive by businesses to merge their AML and fraud processes: the benefits of this integrated, single-view approach are compelling. They include reduced false positives, better use of financial and investigative resources, improved customer satisfaction, radical flexibility to design, simulate, and execute workflows, and – most importantly – reduced operational burden when it comes to compliance.”
At the recent FICO Forum Africa Conference, delegates heard that 31% of South African financial institutions plan to fully integrate their financial crime compliance functions and 38% plan to share resources where synergies exist. Only 8% have no integration plans in place.
“Unified operations reduce costs because there’s no duplication of efforts, they produce faster, more accurate results when it comes to flagging fraudulent behaviour, and they allow for the smarter use of human resources,” says Sander.
Next-generation crime prevention
“Next-generation financial crime prevention strategies will focus on adopting new technologies to build more complex profiles for multiple entities,” says Sander. “This will significantly improve AML processes, because it will reduce the number of false positives, zone in on the truly suspicious cases, and uncover many more that are currently missed by traditional transaction monitoring.”
In a word, a next-generation strategy is ‘holistic’, she says.
“Until now, we’ve addressed financial crime in bits and pieces, using difference data, teams, and processes for AML, fraud, KYC, and transaction monitoring. Naturally, there will be overlaps in these areas, which results in false positives and duplicated efforts.
“By combining fraud and financial crime prevention solutions into a single platform, banks can improve the efficiency and quality of alert investigation and reduce duplication. The result will be greater visibility into risks of all forms and the ability to converge fraud and AML monitoring activities.”
Man meets machine
AI and robotic process automation (RPA) can help break down the silos between risk assessment, monitoring, investigative and due diligence processes, says Sander. With everything in one place, banks are better able to use data-driven insights and skilled resources to determine risk, detect suspicious activity, and prioritise investigations.
“It’s important to note that these technologies will not replace human investigators. The goal of adopting AI and machine learning is to make compliance processes more effective and efficient so that we can use people better.
“While banks are trying to automate as much of the compliance process as possible, they’ll always need humans to teach the machines what to automate. And if they don’t do it properly, then the machines won’t. Over time, machines will become better at identifying new fraud and financial crime patterns across large datasets. Humans can then take the lead and investigate further.”
Next-generation compliance is ultimately about efficiency, intelligence and speed, says Sander. “Using machine learning innovations, we can eliminate unnecessary investigations, detect the unknown, and prioritise alerts. This speeds up time to compliance, improves banks’ reputations, and reduces workloads. Banks can then focus on better serving their customers and launching new products and services to boost their competitive advantage.”