There are various ways of purchasing products and Buy Now, Pay Later is one of them. It is now widely used and is often convenient for consumers. However, BNPL platforms need to be efficient to provide the right services. This is where the facilitation of artificial intelligence is necessary. Through IT infrastructure managed services, best AIOps software, machine learning, predictive analytics, and automation, AI can improve customer experience and ensure a reliable BNPL ecosystem.

A Guide to Buy Now, Pay Later (BNPL)

The Buy Now, Pay Later mechanism allows customers to make purchases and complete the payment for the same later. These transactions are usually interest-free. BNPL has become quite popular particularly due to online shopping and the ease of access it ensures.

BNPL works through a few simple steps. Once a consumer finds a participating retailer, they can opt for BNPL. Upon approval, the consumer must make a down payment, which is usually a small amount. The remaining amount is paid later. The client can opt to pay it in interest-free installments over a period.

BNPL is now being used for various types of purchases and, thus, needs to be managed properly. As the finance industry moves towards a more advanced infrastructure, AIOps digital transformation solutions and other such technology can prove to be impactful in the maintenance of BNPL ecosystems.

AI for a Buy Now, Pay Later Ecosystem

Artificial intelligence can enhance a Buy Now, Pay Later ecosystem in many ways. It can make it more efficient, optimize it, and ensure that all systems are secure. Implementation of AI allows the following:

• Fraud Detection and Prevention

Every industry struggle with fraudulent activities. The finance industry must deal with various kinds of fraud, especially because it processes a large volume of sensitive information, transactions, and personal data. Fraud prevention is, therefore, at the forefront of every company’s task list.

The implementation of artificial intelligence solutions like machine learning in e-commerce and Fintech can help BNPL platforms ensure a robust cyber security system. Machine learning is used to check for deviations from regular patterns and flag them as potentially suspicious activities or transactions which should not be allowed before they have been analyzed. The best cyber security services companies use AI tools to enhance fraud detection and prevention in BNPL ecosystems.

Artificial intelligence can be used for the verification of client data through various services like natural language processing and photo recognition. Fraudulent transactions often happen in repetitive patterns and while it is impossible to check these patterns through human intervention, data analysis can do so within a short period. Predictive analytics algorithms process different forms of data from various sources and use the insights to determine when similar activity has occurred. This helps in real-time analysis and resolution of fraudulent activities.

• Data Analysis

A BNPL ecosystem deals with data from different sources. Some of this data may not be instantly useful, while the rest can provide important insights. Now, it can be time-consuming and almost impossible to process all the incoming data manually. Moreover, human intervention may also lead to errors in data processing, which can have costly consequences. Artificial intelligence helps to streamline this process of data collection and analysis. The use of IT automation with AI allows the collection of data from multiple sources and processing it according to the importance of each category of data. The information collected can then be evaluated and used for risk assessment and to ensure successful customer onboarding.

Risk assessment is crucial for Buy Now, Pay Later platforms. It helps to determine the suitability of clients and provides a general idea of how the collections process is likely to go. To conduct a thorough assessment, it is necessary to have the right data. All data collected by BNPL companies is done so with the consent of the clients. Artificial intelligence tools like AI data analytics monitoring tools, predictive analytics and automation are applied to process and analyze the data. Analysis of this data generates real-time, actionable insights which can be used for various purposes, and risk assessment is one of them.

Many BNPL companies develop risk models with the use of artificial intelligence. These risk models are based on client interactions and add value to customer service. While these models are instrumental in accurate risk assessment, they also help to determine services for ideal clients. For example, suitable clients with low risk can be provided with different products, enhanced loan conditions, cash backs, and various loyalty rewards.

Apart from risk assessment, data analysis within a BNPL ecosystem is essential for providing the right services. Insights into the buying habits of customers can allow companies to make personalized suggestions. These suggestions will show customers many different merchants who engage in similar transactions, thus boosting the revenue for the company through referrals. Data analytics can also help BNPL platforms gain clients. Data from merchant platforms can be analyzed to determine what their customers are looking for, and if the BNPL platform can provide similar services, it is possible to get new customers.

• Improved Collection Process

The Buy Now, Pay Later sector is a part of the lending industry and there is often a problem with collections. The collection process may be hampered if the borrowers refuse to pay back on time, or at all. However, this problem can be tackled with AI solutions like AIOps (Artificial Intelligence for IT operations). Using tools like predictive analytics and real time user monitoring tools, it is possible to monitor multiple borrower channels and establish communication that informs the borrower about pending collections.

Earlier, or in legacy lending, companies could only monitor and communicate with borrowers through specific channels like phone or email. However, a thorough analysis of behavioral patterns on the internet will provide insight into the borrower’s activities. This will allow companies to reach out to borrowers through social media and online advertisements on almost every platform. It not only establishes multiple channels of communication but also speeds up the collection process.

Conclusion

As technology advances, the finance industry is moving from traditional systems to more optimized and efficient infrastructure. This is possible with the help of best AIOps software and digital transformation services and solutions. Such AI-based solutions impact BNPL platforms and create a far more robust ecosystem that is beneficial for clients and is also better at generating revenue.