RPA in fraud detection | Use cases, benefits, and challenges discussed

Originally published at https://www.signitysolutions.com on June 23, 2020, by Ashok Sharma.

The pool of financial services offered to, and engaged by the common public in today’s digital world demand sophisticated systems to collect, connect, and correlate the associated data- or else, these fintech systems would come to an inevitable halt.

That might sound like a derogatory remark on the cutting-edge fintech technologies, such as implementing RPA for fraud detection, but it’s not.

Instead, processes like customer due diligence and regulatory compliance acquire the largest chunk of financial firms’ time, and resources- and we’re not exaggerating on this statement.

Robotic Process Automation presents the potential to effectively automate these processes, and transform them into time- and cost-effective workflows.

What is Robotic Process Automation?

“RPA is the recent development in automation, and a catalyst for the bot revolution.”

In other words, RPA ‘bots’ (software robots) enable the automation of repetitive, often-structured, rule-based business processes. The way they do this is by emulating different human interactions with various software in the legacy system.

No wonder, new-age startups, enterprises, and organizations employ RPA in fraud detection.

However, to put it mildly- it’s a very limited understanding of RPA. To understand Robotic Process Automation in its essence, including challenges, use cases, different applications, and more- head over to this in-depth RPA guide we did a while back.

Why though, do we suggest you check the RPA implementation guide?

For it’ll widen your perspective and enable you to have a better understanding of RPA in fraud detection.

Although RPA is often integrated with multiple banking and insurance processes (see: RPA use cases for F&A ), it discovers new cognitive abilities when integrated with major automation technologies like Machine Learning, Natural Language Processes, and Artificial Neural networks.

Together, these automation technologies automate both structured, and unstructured data processes in lieu of data analysts and other certified FTE human personnel.

Banks, Insurance Companies, and other financial institutions employ this new-age RPA technology to identify, and counter frauds- pulling data from multiple service lines, instead of creating a bunch of financial macros .

Now the skeptics amongst you might argue that when we have conventional financial macros, why use RPA in fraud detection?

Here are your answers (though, be aware-it’s subjective)

Identifying, combating, and curbing fraud through conventional automation techniques poses not one but several problems. For starters, traditional automation and other financial macros are inflexible, for they lack core options like cross-integration, real-time behavioral profiling, data analyzing, and more.

With this basic understanding, let’s ascertain the ways on how RPA aids in identifying and curbing financial frauds.

How RPA is used to fight financial fraud?

Contrary to the popular belief, you can’t substitute your entire workforce for RPA bots. That is to say, if you believe that you could assign a desk to the Robocop and profit of its benefits- you’re probably mistaken.

As stated above, RPA refers to the software bots that automated repetitive, and mundane tasks that are too monotonous for any human.

The benefit? Cheap solution. Faster Processes. No Clerical Errors.

Introducing automation technologies to the financial aspect of any business, implies to leveraging their cognitive capabilities, and train RPA bots to search for and scrutinize processes that involve identifying, tracking, and flagging fraudulent activities.

Here’s how RPA can help mitigate fraud risks:

1. Reassessing current processes

RPA bots can be automated to review the current and former financial transactions, on a timely basis, to identify uneven patterns indicating illegal (often fraudulent) activities and piracy.

Even If we were to consider a general case scenario:

RPA implementation requires financial institutions to thoroughly understand, document, and evaluate the processes that present the highest cost-benefit potential.

As a financial professional goes through these stages, they develop deeper insights into business processes and identify high-risk financial areas.

Either way, these efforts assist business instigators and financial professionals to identify the vulnerabilities and curb frauds with business processes.

2. Eliminating human errors

It’s preemptive to identify the right opportunities to conduct financial fraud. In simple terminology-

“Fraud is a crime of opportunity.”

For such opportunities to arise, employees either have to constantly interact and tinker with financial processes, or miss out on subtle yet critical details- creating a gap.

These gaps, if and when identified, could result in massive financial losses, and in worse-case scenarios- force businesses to dissolve assets and terminate their operations.

However, when a business strategically integrates RPA to well-thought and designed business processes, human interaction is significantly diminished. The result?

Business employees can shift their focus on other high-priority tasks, limiting interaction with high-risk processes that involve numbers, and other critical data.

An added benefit- with lower human interactions there’s a significant drop in clerical errors.

3. Enhanced trade monitoring

With money laundering on the rise, major industries, and even Nations are taking intelligent automation initiatives to battle financial terrorism and money laundering. Which technology do you think these entities employ to tackle these financial frauds?

Robotic Process Automation. RPA-integrated software channels are highlighted on the automation podium.

RPA bots, when integrated with other automation technologies, can evaluate transactions for potential frauds, and flag high-value transactions to perilous sectors/areas.

Given the ability to scrounge for disorganized information and unstructured data, RPA bots can handle such critical situations with greater accuracy; far better than any FTE employee.

4. Automate temporary block removals

Banks operate and handle hundreds and thousands of accounts- a majority of which remain sedentary for months on end. At times, when banks suspect any suspicious activity with these accounts, they place them under temporary blocks.

These temporary blocks age out, but the blocks remain- unless they are manually unblocked by the financial professionals.

RPA bots can be effectively used to identify accounts with these blocks, access their past activities, and remove the restrictions. But it’s only possible if the account activities comply with the established criteria for the block removal.

5. Automated threat detection

“RPA bots work exceptionally well with structured data.”

Monitoring thousands of websites is be a big deal for a human but is comparatively nothing for an RPA bot. Let’s take two case scenarios where RPA can assist with automated threat detection:

6. Copyright infringement

RPA bots prevent copyright infringement by quickly monitoring the suspected websites for your patents, trade secrets, and other crucial data.

7. Product pricing

To boost sales, several companies might sell your products at a lower price (one that’s technically not feasible to match). RPA bots collect and aggregate pricing data, to check if your offerings are being unethically sold online, or below the set MSRP.

Is RPA the best solution for Fraud Detection?

We wish the answer was a plain yes or no, but RPA crosses the box for both classifications when it comes to fraud detection.

RPA started as a basic automation technology but has evolved into a full-fledged autonomous automation system- capable of understanding unstructured data and calling the shots.

If we take simple RPA automation, it might not be as effective for detecting frauds or recording uneven patterns. But things take a turn when we employ these simple RPA bot, but with machine learning capabilities.

Machine learning algorithms perform consistent tests, with the sole purpose of identifying abnormalities in transactions, and associated patterns; enhanced cognitive abilities is what help these bots to decipher and report these financial anomalies as fraudulent activities.

The challenges of using RPA in fraud detection

Robotic Process Automation offers extensive benefits to businesses- fraud detection being one of them. However, considering and implementing RPA in fraud detection, and curbing financial fraud risk processes, requires in-depth subject matter knowledge, expertise, and rigorous monitoring.

You could afford to do that in-house, but that’s not advisable.

Why?

Implementing RPA solutions, and upgrading the infrastructure isn’t a cheap affair. The better alternative is to hire RPA Implementation experts and let them assess automation opportunities for your business processes.

Although a little expensive in the initial implementation phases, RPA offers banks, financial institutions, and even your business a competitive edge. It not only helps with fraud detection but ensures that your business ignores futile attempts, keeping costs at a bare minimum.