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Leveraging Data and Analytics to Counter Credit Card Risks and Manage Frauds

The banking industry has become intensely competitive, with newer players entering the market and offering customised financial services. However, the credit card industry is facing turbulent times with increased federal regulations and generation of record charge-offs. Is it possible for credit cards to still be profitable as a standalone product? This question brings the future of the credit card industry into focus - the tug-of-war between more diversified banks and monoline card issuers. A complete transformation of processes is the likely solution, with all issuers opting for a mixed approach - digitising, automating, and innovating a few processes while completely ignoring other legacy systems.

Challenges in managing credit risk

The global credit crunch that emerged in 2008-2009 (when outstanding credit card debts crossed $950 billion) is still looming - global debt went past the $246.5 trillion mark hallway through 2019. In a climate of vicious economic cycles where people and businesses have to fall back on credit to meet their daily needs or to maintain their living standard, credit risk has become a huge burden, eroding the profits of credit card companies. To manage credit risk, organisations must overcome the following challenges:

  • Managing data for actionable insights:

    For companies, managing huge amounts of data and deriving insights that can be deployed for assessing credit card risk is a mammoth task. With millennials adopting digital payments more and more, banks need to derive meaningful conclusions from their data streams with the help of AI-powered data management solutions to personalise their consumer engagement.
  • Strategizing to combat the lack of resources:

    When it comes to devising a strategy for managing credit risk, it is crucial that companies possess tools that aid in visualisation and focus on crucial information while ignoring irrelevant information.
  • Identifying and adopting accurate rating models:

    The accuracy of rating models to assess credit risks has always been an issue, making it difficult for lending businesses to predict a borrower’s attitude towards regular repayment or defaulting on a loan. The adoption of fintech to reap close to accurate results is key to today’s successful credit scoring models. These can empower businesses to better assess investment risks and risk profile of individual customers.

Companies need to set up a strong risk awareness culture that maps the early triggers to reduce the risk to the greatest extent possible. Distressed credits should be given timely attention - a specialist team needs to monitor these activities.

With digitalisation bringing more of the unbanked lower income groups into the banking system, the credit card industry can foresee an untapped, potential consumer base. Now, their credit risk model can be determined, and they can avail financial services. Transformation, more than just a buzzword, when implemented effectively, can assist departments like marketing and sales to team up with the credit risk department to provide services to new consumer segments and locate these potential users of credit cards.

The top brass from credit card companies need to ensure that their processes eliminate the "noise" and avoid fraud - enabling the right product to fall in to the hands of the right consumer.

While people increasingly favour the use of plastic money over cash, the risk of fraud and identity theft are on the rise. Even with strict access barriers and smarter consumers, data theft is an ever-present danger, and the credit card industry is bearing the brunt.

Apart from managing defaulters, financial institutions that offer credit card services have a new challenge - protecting their consumers’ data and reducing the risk of data falling into the hands of cyber criminals. In fact, a survey estimates that, by 2020, losses due to credit card fraud are anticipated to reach 36 billion dollars.

Why is it challenging for companies to manage financial fraud?

  • Lack of predetermined methodologies to assess losses caused by such frauds makes it difficult to quantify it.
  • Absence of preventive measures in place, such as a sophisticated software that can aid in fraud detection.
  • Presence of multiple business functions that are spread globally, which make it difficult to devise a centralised approach that is compliant with all the relevant local regulations.
  • Absence of universally accepted solutions for detection and fraud management on the internet - now, businesses are looking at smarter AI-rooted solutions to detect new attacks and stop losses before they happen.

How can data analytics help credit card organisations with risk control?

Analytics is key to understanding very specific customer needs and expectations and then mapping consumer engagement with them. Capturing, integrating and analysing data from various internal and external sources can present a holistic view of a customer, assisting businesses in redesigning their core processes to cater to these expectations effectively.

To pinpoint users who are likely to default, companies need to map behavioural patterns of cardholders with their buying and spending behaviour. With the availability of automated tools to reduce the scope of manual error, this saves time and effort to a great extent.

Risk control can be significantly improved with the use of analytical techniques such as credit collection analytics and next-generation stress testing.

Another technique is to focus on consumers who have had a positive repayment record and target newer products at them. This enables companies to dive deeper into the detailed profiles of consumers.

Analytics can assist in exploring untapped markets with high potential for growth, and in diversifying risk.

A question every credit card company needs to ask is whether they have a robust strategy in place to mitigate the risk of fraud? In an era of AI and machine learning, analytics tools have reached a new high. They can be deployed to fight bank frauds and ensure timely detection of frauds. Analytics can provide a holistic solution, which addresses all the challenges mentioned above.

It is crucial to have a trusted partner who can assist you in developing strategies to manage the humungous amounts of data, and in employing analysts who can deduce meaningful conclusions for the banking industry.

Apart from helping you focus on your primary business areas, this expert vendor can serve as a checkpoint for your risk areas, alerting you in a timely manner, saving the huge costs involved in regulations, and helping nip the issue in the bud.