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Machine Learning: Improving Efficiencies in Payments

April 29, 2022


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The use of machine learning in the payments business can assist established banks and FinTech companies in overcoming challenges. Let’s take a closer look at what role ML can play in improving the performance of the payments industry.

The overall relevance of digital payments has increased significantly in the past couple of years. This has been spurred on due to the pandemic’s impact and the improvements in the technology available. As per a PwC report, the total digital payments worldwide are expected to increase by almost 80% between 2020 and 2025. The overall FinTech and digital adoption rates have also grown consistently. This can also be accredited to the significant improvements in various areas such as the security of transactions, processing speed, and reduction in payment failures.

The introduction of ML and AI processes to different aspects of digital payments has helped facilitate transaction monitoring and real-time authorization, further improving the customer experience to a great extent.

As a result, here are a few factors that underline the enhanced role of technology in digital payments:

Improved Predictive Analysis

The machine learning process’ accuracy improves with the overall increase in data availability. In 2021, the total value of the digital payments market was a staggering $7.36 trillion. However, the market’s overall size is expected to increase further, meaning more people are to embrace online and digital payments, improving data analytics and machine learning processes remarkably.

One of the various impacts of this could be improved predictive analysis that includes enhanced credit card behavior prediction for users. It will help FinTech companies and banks understand consumer payment preferences and spending habits, which can create a transaction-driven marketing solution. In addition to this, the predictive analysis shall be crucial in facilitating the users’ credit score management — another area in which FinTech companies are expected to thrive.

Fraud Detection

The rise in digital payments worldwide has been accompanied by an overall increase in the total number of digital frauds. In the E-commerce segment, the total amount of frauds went up to $20 billion in 2021. Hence, to ensure that the overall growth of digital payments and E-commerce, on the whole, can be continued, the fraud detection methods must be improved. ML and its role in fraud detection are currently underrated, and the future of the overall digital payment industry depends on how efficiently such processes are improved.

ML-based processes can be used for fraud detection and avoidance, by introducing fraud detection algorithms using large volumes of digital transaction data. With the increased number of online digital payments and transactions, there is a significant volume of digital transaction data availability, which further improves the fraud detection capabilities facilitated by ML. In this scenario, the role of both supervised and unsupervised algorithms will be crucial, and any suspicious activities could be analyzed through the algorithms. To explain further, supervised ML is trained in ‘labeled’ data, and based on the dataset, the algorithm predicts the output. In contrast, unsupervised ML is an algorithm that learns from untagged data. Hence, the expanded role of machine learning in digital payments will ensure that the payment industry can process even larger payments while demonstrating low error rates.

Reducing False Debit and Credit Card Declines

Another area marred by issues within the payments industry is the false debit and credit card declines. There could be different reasons for such declines. Still, eventually, it frustrates the users and banks alike, and the financial institutions’ reputation is often affected when such issues arise. It is estimated that companies lose around 3% of revenues each year due to false card declines, i.e. when a correct and legit transaction is flagged as a fraud. ML-based algorithms can be used to identify transaction anomalies rather than the traditional rule-based technique that can result in rejecting a non-fraudulent transaction.

Usage in Banks and FinTech Companies (Internal Processes)

In addition to facilitating the transactional issues mentioned in the previous points, the usage of ML in the payments industry can help address existing banks’ and FinTech companies’ challenges. This includes facilitating document extraction, which could be faster and more reliable by introducing vision and NLP techniques. The function of claims processing could also be enhanced, which could be crucial for insurance verification and subsequent digital payments. In addition to this, KYC verification and client onboarding could be entirely automated by using machine learning processes within the payments industry.

In Conclusion

The payments industry has outgrown all expectations and has developed consistently. The digital adoption rates are high due to the security and quality of services offered by banks and FinTech companies. However, the industry still faces several challenges that the ‘evolved role of ML’ in the next decade, will be able to resolve.

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