I will never forget Summer of 2021, where I had regulatory audit of AML/CFT. We never had thought that Scenarios which  will help us in transaction monitoring can become a  trap. We proudly spoke with twinkle in our eyes in our preliminary meeting with regulators that, we have most robust AML/CFT system. Later they shown us what our transaction monitoring has missed. I felt a chill run down my spine at the thought of consequence. It was bad, really bad. I had to provide written explanation to my organization that it won’t repeat again and I would make full commitment to improve transaction monitoring. After that incident, I realized for effective transaction monitoring, we don’t need a lot of scenarios, what you need to understand is what story your transaction data is telling you? So, are you willing to hear that story.

 

In the ever-evolving landscape of financial crime prevention, staying ahead of illicit activities like money laundering is paramount. As regulatory requirements tighten and criminals become increasingly sophisticated, financial institutions are turning to advanced technologies to bolster their defenses. One such technology making waves in the realm of anti-money laundering (AML) is data visualization tools like Power BI. In this article, we delve into the pivotal role Power BI plays in transaction monitoring for AML, empowering institutions to identify and combat suspicious transaction patterns effectively.

Understanding the Challenge:

Money laundering poses a significant threat to the integrity of the financial system, allowing criminals to conceal the origins of illicit funds. Traditional methods of detecting suspicious transactions often rely on manual processes and rudimentary tools, making it challenging to uncover intricate patterns indicative of money laundering activities. Moreover, the sheer volume of transactions processed daily further complicates the task, increasing the likelihood of oversight and false positives.

The Power of Data Visualization:

Enter Power BI, a powerful data visualization tool that empowers financial institutions to gain actionable insights from vast amounts of transactional data. By leveraging intuitive dashboards and interactive reports, Power BI enables analysts to identify anomalies, trends, and patterns that may signify suspicious behavior. Through dynamic visualizations such as charts, graphs, and heatmaps, complex data sets are transformed into digestible insights, facilitating informed decision-making in real-time.

Case Studies and Success Stories:

Several financial institutions have already embraced Power BI as a cornerstone of their AML compliance efforts, witnessing tangible benefits in terms of efficiency and effectiveness. For example, a leading bank deployed Power BI to analyze transactional data across its global operations, resulting in a 30% reduction in false positives and a 50% increase in the detection of suspicious activity. Similarly, a fintech startup leveraged Power BI to monitor peer-to-peer transactions, enabling proactive identification of money laundering schemes and enhancing regulatory compliance.

Let’s demonstrate with the following example:

Meet Rahul. He owns a clothing store, Mahima Impex and dreams big. Recently, he got a machine that helps customers pay with their credit cards. It’s called a POS machine, and it’s like a cash register.
One day, a man named Mr. Tulsi Ram comes to Rahul’s shop. He seems friendly and buys a lot of clothes. While chatting, Rahul mentions his dream of expanding his store but says he needs money for it.
Mr. Tulsi Ram then tells Rahul something shocking – he admits he smuggles gold illegally. He says he wants to clean his dirty money, and he has a plan. He suggests using Rahul’s POS machine to swipe credit cards and take out cash. In return, Mr. Smith promises to give Rahul 20% of the cash.
Rahul gets excited about the offer because it sounds like easy money. He agrees without thinking much.

Now, let’s us imagine, we don’t know such transaction is happening within our organization how we are going to identify it using  Power BI?

Credit Card Transaction Visualization

Now let’s break down this visualization to identify suspicious transaction mentioned in above story:

1. In this figure, Credit Card transaction of the customer Tulsi Ram has increased from month of August and same amount of credit card transaction has been observed for the month of September and October.

2. In this figure, Credit Card Transaction is matched with Credit Card limit assigned to the customer. There is only single line meaning customer has used up all his credit card limit meaning for every month his credit card transaction is equal to credit card limit. The credit card limit of the customer is given in figure 3.

5. This figure shows biggest red flag, here the map shows the location from where customer has performed his credit card transaction. Here, it is single point meaning that customer has performed credit transaction from a single place that MAHIMA Impex(fig 4) as mentioned in our earlier.

By looking at this visualization, one can see that customer is doing some fishy transaction as why he is performing all credit card transaction from single place and also he has used up all his credit card limit.

There is another benefit from this visualization, now, you can create scenario like: “Generate Alerts for those customers, who utilizes more than 80% credit card limit by doing credit card transaction from single location”.

If you create such scenarios, you can be rest assured, that almost 90% of your alerts lead to Suspicious Transaction Reports.

 

Radhika is a homemaker who isn’t well-versed in banking matters. Her husband, Ramesh, instructed her to open a bank account. He requested all the checkbooks, mobile banking, and internet banking credentials from her. Unknown to Radhika, Ramesh’s real intention behind having her open the account was to channel his business transactions through it, utilizing her account for the purpose of tax evasion.

Now, how can you identify red flags in this situation using data visualization tools like Power BI.

Overall Customer Transaction
  1. In this figure, customer total debit and credit transaction way more than the annual income of that customer.
  2. The cash transaction both deposit and withdrawal is around 800k to 1 million.
  3. The online transaction is about 20 million.

Now based on our story, customer is housewife. Housewife usually don’t have fixed source of income, they depend upon her husband or other family relative to provide them with income source. So how come, there is so much transaction in her account. This definitely means that there is presence of hidden beneficial owner, that is her husband, who is routing his business transaction through her account so that his business income is under reported and his tax liability will be less, thereby, leading to tax evasion. So we can safely file this account as suspicious transaction report against this account, if her husband is unwilling to provide his Know Your Customer Information.

Finally, after identifying such suspicious pattern, we can create a scenarios to monitor housewife account to see if there are other cases or not. One such scenario could be, if there are transaction above 50lakhs in housewife account we can treat that account as suspicious.

Key Features and Benefits:

  1. Advanced Analytics: Power BI’s advanced analytical capabilities, including machine learning algorithms and predictive modeling, enable institutions to uncover hidden patterns and predict future trends in transactional data. By analyzing historical transactional behavior, Power BI can identify deviations from normal activity, flagging potentially fraudulent transactions for further investigation.
  2. Real-Time Monitoring: In the fight against money laundering, timeliness is critical. Power BI facilitates real-time monitoring of transactions, allowing institutions to detect and respond to suspicious activity promptly. Automated alerts and notifications can be configured to trigger when predefined thresholds are met, ensuring swift action to mitigate risks.
  3. Customizable Dashboards: Power BI offers unparalleled flexibility in dashboard customization, allowing institutions to tailor visualizations to their specific AML requirements. From transaction volumes and geographic trends to customer behavior analysis, Power BI empowers analysts to create personalized dashboards that provide comprehensive insights into suspicious activity.
  4. Seamless Integration: Integration with existing data sources and systems is seamless with Power BI, streamlining the data aggregation process and ensuring a unified view of transactional data. Whether data resides in legacy systems, databases, or cloud platforms, Power BI can consolidate disparate sources into a centralized repository for analysis.

Transaction Monitoring using Power BI Online Course

Conclusion:

In the battle against money laundering, data visualization tools like Power BI are invaluable allies, empowering financial institutions to stay one step ahead of criminals. By harnessing the power of advanced analytics, real-time monitoring, and customizable dashboards, Power BI enables analysts to uncover suspicious transaction patterns and mitigate risks effectively. As regulatory scrutiny intensifies and criminals devise ever more sophisticated schemes, the role of Power BI in AML transaction monitoring will only become more pivotal, safeguarding the integrity of the financial system for years to come.

 
 

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