Financial institutions and other organizations that have been susceptible to financial crimes have adopted various robust practices to protect them against financial crimes.
Earlier, the anti-financial crime teams only consisted of investigators and analysts, but with time and technology enhancements, analytics experts have become pretty standard.
Due to the lack of subject matter expertise within organizations, anti-financial programs were conducted to form focussed teams that were adept at data analytics.
Later on, the compliance costs increased, and because of the enhanced usage of advanced AML analytics and machine learning, substantially significant analytics groups have been created.
Even with the new enhancements, there are several challenges faced with this current approach. Some of the critical challenges that hamper the effectiveness and productivity of analytics programs are:
- Limitations for coding languages
Usually, the typical systems allow the programmers to code in only specific languages such as Scala, Python, R, and SQL.
- No availability of visualizations
The analytics team finds it difficult to understand specific data and investigation outcomes because of the lack of visualizations.
- Issues with documentation and governance
The institutions are required to maintain complete audit trails as well as documentation of the modeling process. This feature is unavailable in standard modeling tools, and it forces one to manage two different systems, where one is for modeling, and the other is for modeling documentation.
- Expensive
Due to multiple systems and integrating them all for operationality, these analytics initiatives become costly.
Top 5 Effective Solutions for AML Challenges
Here are the most effective AML analytics solutions to enhance the productivity of discovering financial crimes and analyze their patterns.
- Integrated Platform
Usually, documentation and modeling are handled with the use of two different systems. It is the reason why there was a need for manual integration, and it also hampered efficiency. As there is a continuous change in criminal behavioral patterns, it has become essential to implement ongoing discovery and modeling of new designs.
Machine Learning and Graph Analytics turn out to be pretty much useful for finding new criminal behavioral patterns. With the help of an integrated platform, the data scientists can effectively discover and analyze different patterns to enhance productivity.
2. Visualization
The work of the analytics team becomes pretty much easy and effective with the help of proper visualization tools. The analytics team could obtain a meaningful output for understanding the output with enhanced efficiency.
Graph analytics has become an effective method for analyzing intricate money movement patterns and identifying various suspicious activities. Analysts and Data Scientists could interactively obtain the data and insights to explore the prevailing financial crime patterns and trends.
3. Centralized Data System
Every financial institution needs a centralized data system to effectively discover criminal behavioral patterns. A centralized system will allow the analysts to use all the data such as accounts, transactions, cases, and any other data related to the financial crime for getting insights. It would reduce the time and efforts spent by the Data Scientists for gathering data for analysis.
The data might be spread in different types of systems, and connecting all this data becomes difficult. The IT team would focus on data analysis as the centralized data system will handle merging the data from different sources and making it available in a single place.
4. Enhanced Cooperation
There is a huge gap seen in the communication between AML departments in different enterprises. With cross-system integration and a clear accountability system, the loopholes existing between the departments could be filled. It would also reduce the chances of potential attacks and ultimately reduce financial crimes.
5. Effective TMS Agility
The outdated Transaction Monitoring Systems (TMS) monitors a massive amount of data for identifying the attackers, but smart attackers bypass this system with new strategies. Enterprises could deploy new data sources, algorithms, and detection scenarios for discovering new threats effectively. With rapid functioning and flexibility, TMS monitoring could be made more productive as well as effective.
With the era of digitalization booming in the world, the number of transactions has drastically increased. It has resulted in enhanced risks against different criminal behavior patterns that are changing as per the time. AML analytics has to be included to discover and analyze new criminal patterns used in financial crimes to stay one step ahead of the attackers.