FinTech : Avoiding Fines with Partial Automation

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A bitcoin spider (it does not exist)


Fintech is an area of the financial industry that is constantly evolving and full of excitement and confusion.

While you may enjoy the convenience of tapping your card on a point-of-sale terminal, your granny may struggle to adapt to the bank's new app.

We can all agree that technology is infiltrating every sector and has led to the emergence of cryptocurrency and autonomous vehicles. 

However, with innovation comes the need for regulation, and companies in the United States are now subject to more than 50,000 regulations according to page 6 of SVB's Breakdown on The State of Fintech.

Failure to comply with these regulations can lead to severe penalties, damage to the company's reputation, and even legal action.

Coinbase is a prime example of this, as they were fined $50 million for failing to build and maintain a compliance program that could scale with their rapid growth. 

Today, risk management is still a highly manual task that requires a large workforce to monitor and respond to all potential breaches.

In Coinbase's case, they had a monitoring system that generated over 100,000 alerts about suspicious transactions.

However, it was impossible for humans to process and address these alerts in a timely manner. 

In the era of Big Data, where 188 million emails are sent every minute (as of 2019), relying solely on a monitoring tool is no longer sufficient.

More attention must be placed on proactively responding to issues related to data breaches, fraudulent transactions and money laundering.

As resources are limited, finding a way to differentiate risks that require human intervention from those that can be processed by artificial intelligence would be a valuable addition to the risk management in the banking sector.

Here are some ways to partially automate the risk assessment process: 

1) Automate Workflows

Workflow automation involves automating the sequence of tasks involved in a process.

This can help ensure that tasks are completed in  an efficient manner. 


2) Implement Machine Learning

Machine learning algorithms can be used to analyze data and identify patterns that may indicate risks.

 By training the algorithms on historical data, they can learn to identify potential risks more accurately and quickly than humans.


3) Use Data Visualization Tools

Data visualization tools can be used to create interactive dashboards that display key risk metrics in a visual format. 

This can help organizations quickly identify areas of risk and take corrective action as needed.


Resources 





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