Artificial Intelligence in payment technology, can play a very crucial role in improving fraud detection, and help FinTech startups, banks, and social media payment systems. Improvement of fraud detection in banking using machine learning while reducing time. Here is my take on it.
Tech adoption is very high at FinTech startups, banks, and social media payment systems, and for good reason as the consumers who use such products demand better, and secure service. A lot of monetary transactions happen from single-digit dollar values to multi-million dollars on these platforms. It is very important to make sure that there is a state of the art fraud detection and security system in place when dealing with transaction volume and dollar value.
AI and ML can be a great help to these financial institutions, organizations to improve their security systems and fraud detection. AI and ML have recently become the very crux of innovation in the FinTech sector, and many financial institutions and organizations have started integrating it into their services.
It helps them take advantage of the efficient and effective tech, use predictive modeling to improve their products and provide better financial advice to their customers while minimizing the risk of fraudulent behavior.
This is a win-win approach for the financial institutions, startups, social media payment systems, and their customers.
A better approach to financial advice
Modern technology like Artificial Intelligence, Machine Learning, Automation, Big Data Analytics have helped financial advisers in a broad spectrum of financial management to extract crucial insights out of vast and complex data. This data has enabled them to make better predictive models to make informed recommendations that not only helps the organizations but also the customer who uses their service.
Some startups are using this approach to provide better financial advice and leveraging these technologies to efficiently improve their transactional streams, which is in-turn helping their customers.
Using this approach and automating this process is enabling financial institutions to provide this service around the clock without a lot of capital investment in the financial advisers. Tools that are interface based are built using NLP. These AI-based financial advisers that interface with their customers can understand a customer’s request and provide them with the data they’ve requested. Once the data requested is available, complex AI algorithms can run on the data set and help with recommending financial strategies to users. One good example is helping users understand their spending, income, and recommending strategies based on the savings to improve their financial standing. This model is scalable to organizations that are in need of financial strategy.
It’s very important to integrate this interaction into products in a very seamless manner. There should be a seamless transition between machines and humans during the interaction with the client when needed.
Understanding risk profile
AI can be a great tool to improve the process of profiling the client/customer based on a diverse set of data that can help the bank/organizations make a much more informed choice and prepare a personalized plan for every customer. There is a broad spectrum of customers with a diverse set of situations, with AI and the data sets the banks/organizations can provide a personalized plan for every customer. This approach not only helps the banks/financial facilitators but also, the end-user the customer.
AI allows financial institutions, facilitators, to automate this process, thus making decision-making much faster and more precise. This categorization, allows them to calibrate the services they advertise to their customers. These models are typically based and trained on actual customer data collected throughout the years, which ensures that the banks will be able to make their individualized offers with maximum precision and relevance.
Detecting fraud and managing claims
AI-based tools are now implemented to gather evidence and provide banks and Fintech startups with the necessary data to allow them to identify fraudulent behavior or transactions. Using AI can help banks and social media payment systems to not just detect the fraudulent behavior but also predict before any fraudulent behavior happens based on pattern mapping and chronology of events. This can save customers, banks, social media payment systems a lot of money that can be lost due to fraud.
AI, for example, can help these institutions start building a comprehensive mix of multi-factored logistical regression to create new dynamic weights for each data point when considering a transaction. Most critically, systems can learn from each transaction, constantly improving and becoming more effective, something that is unique to machine learning and AI. In short, the use of AI can allow payments companies to look at transactional data in an efficient manner, growing the number of successful true transactions while shrinking the number of fraudulent ones that make it through.
Further, AI and machine learning technologies have elegant applications to the world of underwriting, If mobile payments, cashier-less checkout, and value-added solutions are tailor-made for any payments tech group, then they can be enjoyed only with a healthy dose of underwriting.
AI can help payment companies underwrite the merchants they onboard by creating a standard that is constantly changing, adapting, living in real-time, and adjusting with every chargeback and instance of fraud. This not only creates a safer ecosystem but also helps payments companies protect themselves from losses due to fraudulent merchants in a way that was impossible by manual review.
Artificial intelligence in payment technology.
Payment Industry is on the cusp of reforms and financial organizations/banks, are eager to implement AI for efficient payment processing, increasedStraight Through Processing (STP) rates, drive incremental enhancements to user experience, and gain an early advantage. There are many areas within payment processing where AI can take the product to the next level. From a holistic viewpoint, AI can be applied in payment processing at two levels.
At individual application level such as Fraud Analysis, Payment Validation, Payment Enrichment, Payment Repair, Selection of a method of payment…etc. Currently, all these applications are rule-based. They are a set of rules deciding the consequences based on the rules set and actions taken. These should be dynamic and always adapting to real-time situations.
The advantage AI brings here is the power of seamless decision making backed by deep analysis of payment trends, payment behavior, and historical data. It can drastically reduce manual intervention in payment processing and enhance STP rates. Tactical solutions such as Robotic Process Automation (RPA) can leverage AI to effectively manage routine operations. There has to be a seamless integration of human intervention where needed.
AI systems can monitor payment transactions from the point payment message enters the bank until it leaves the payment gateway by monitoring actions at a process level and suggest intuitive services and offers. With the access to feeds from the financial market on the latest trends and process improvements in other banks etc., AI systems can suggest suitable payment products/plans for the customer in terms of processing time, payment charges, and payment usage customized to customer’s activity patterns, which will in turn help in customer satisfaction and customer retention. This can be an initial step towards personalized variable payment plans which will also be customer focussed.
Variable payment plans
AI as a technology can help the banks and financial institutions come up with personalized payment plans based on every customer’s capacity that suits the rate of payments to the bank and also helps to ease the payment burden on the customer. This would mean, variable mortgage payments, variable subscriptions, variable tax payments… etc. This can revolutionize the payment industry in a manner that was not possible with manual/semi-automated systems currently in place.
Conclusion – Artificial intelligence in payment technology.
These were the most intriguing AI applications in Fintech to this moment. However, it’s essential to stress that there are a lot of issues AI needs to deal with such as bias, ethical practice, and data corruption. Although AI has tremendous advantages and possibilities to improve existing product offerings, it also poses a great risk not only to the service provider but also to society at a higher level.
I am positive that role of artificial intelligence in payment technology, that is constantly expanding and evolving on a daily basis and is projected to improve our experience. This could be a win-win situation we have been waiting for.