ML Compliance Risk Monitoring

Proactively identify compliance issues and risks based on your corporate policies using machine learning

compliance

Advanced identification of risky behavior

Timely reaction to potential breaches

Problem

The pharmaceutical industry is one of the most highly regulated ones, not only in terms of drug production and trials but also in the way they are distributing products within the market. Oftentimes the sales representatives negotiate their deals during business meetings or business meals; however, rules need to be followed in order to avoid these conducts from falling into the realm of bribery or foul play. We had a client in the pharmaceutical industry that understood the risks and had set up two systems to keep track of those business transactions – one for meetings and events and another for meals and gifts. The issue they were facing, however, was that it was time-consuming and difficult to spot any risky transactions. So, they turned to us to help them make this process more manageable.

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Solution

To help our client, we built a machine learning algorithm that took the data from their two systems and analyzed it all from the start to find any dishonest patterns among the sales rep transactions. When anything was deemed suspicious, our algorithm would highlight it for an easier examination afterward by the appointed body. All the data was collected and examined within a dashboard we created as well so that analysis and reports could easily be made when needed. Most importantly, the spotting and highlighting of potential compliance breaches were made so much quicker through our algorithm that no human could reach the same accurate results within a reasonable timeframe.

Success

With the help of our solution, our client had all the compliance risk preventive power in one easy-to-access dashboard just a click away. It used to take them weeks beforehand to spot any potential risks or breaches within the reported transactions, but now they have a report ready by the end of the day. Additionally, they didn’t need to set aside as much manpower to manually go through all the data, because now the algorithm highlights all the transactions worth investigating.

compliance