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Ten FinTech Use Cases that you need to know

Ten FinTech Use Cases that you need to know
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by Sanjeev Kapoor 12 Dec 2022

Financial technology, or fintech, is a term used to describe a range of digital companies that offer financial products and services. These companies generally fall into various categories, including for example technology-enabled banks, online payment companies, and marketplace lenders. Fintech firms do not simply employ technology to offer financial services. They use technology to solve problems in innovative ways. In this direction, financial technology empowers financial institutions by making available a vast array of innovative tools and systems that, when used responsibly, can make financial processes safer, easier and more efficient.

Financial technology enables financial organizations of all types and sizes to innovate in different areas. Fintech use cases span the entire value chain ranging from the front office (e.g., interaction with customers and institutional investors) to the middle office (e.g., ensuring that trading activities across markets are closely monitored for potential risks and that mitigation strategies are deployed when needed), as well as to the back office (e.g., processing transactions related to payments and providing customer centric products). Following paragraphs illustrate ten of the most popular Fintech use cases.


1. Smart Payments

Many fintechs provide innovative payment platforms that execute payments in faster, more intelligent, and cost-effective ways. For instance, there are fintech firms that facilitate global payments, given that the latter are generally slow and have high commission fees. Moreover, there are other enterprises that provide new payment channels to increase customers’ convenience such as payments via mobile wallets, SMS services, and payments integrated within utility bills. Most of these payments’ services leverage novel technologies such as mobile computing, payment gateways, omni-channel transaction platforms, as well as smart contracts and blockchain technology.

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2. Robo-Advisors

Roboadvisors are bots that provide automated and personalized investment advice to customers that wish to purchase assets and securities. They use advanced analytics algorithms over large amounts of data about the customers and the status of the market, to recommend optimal investment decisions in-line with the customer’s risk profile. In many cases they also consider alternative data (e.g., open data, social media data) to extract insights about the market (e.g., market sentiment) and the customer’s risk appetite. In most cases, roboadvisors make use of BigData and Artificial Intelligence (AI) technologies to automate data analysis. In recent years, the scope of robo-advisors has been also expanded towards real-time decisions that are destined to support high frequency trading such as Forex.


3. Regtech

Regtech stands for Regulatory Technology. It refers to a specific fintech segment that employs technologies in order to improve legal and regulatory compliance processes. For example, thanks to machine learning algorithms, financial institutions can identify hidden patterns of non-compliance, beyond standard regulatory rules. Moreover, modern AI technologies like Natural Language Processing (NLP) automate the process of inspecting documents and provide opportunities for extending regulatory checks (e.g., Anti-Money Laundering controls) to unstructured data sources like news and social media information. Also, Regtech enables real-time processing of financial transactions at the finest possible granularity i.e., it is possible to audit each individual financial transaction for compliance. This is a significant improvement over state-of-the-art regulatory processes that tends to look at batches of transactions.


4. Credit Risk Assessment and Credit Underwriting

Machine learning techniques are nowadays used to foster a novel statistical approach to assessing the creditworthiness of individuals and businesses. Rather than relying on conventional credit reports, statistical approaches consider a broader range of available data about the customer. In this way, fintech approaches to credit risks assessment hold the promise to overcome the limitations of state of the art credit risk assessment processes, which are quite stringent and exclude small medium enterprises from access to finance. Likewise, AI and ML technologies can be also used in automating credit underwriting processes, as well as in increasing their accuracy and speed.


5. Know Your Customer (KYC) Use Cases

Many fintech banks leverage AI technology to ease and accelerate the ever-important KYC process. For instance, AI tools are used to identify regulatory changes in order to automatically request customers for new KYC information. Moreover, most fintechs employ highly automated customer on-boarding processes that do not require the customer’s physical presence in a bank branch. For instance, various technologies are used for the automated recognition of documents (e.g., passport images) in conjunction with biometric authentication.


6. Front Office Automation

Nowadays, chatbots and voice assistants are increasingly used in the front office of banks and financial organizations. State of the art chatbots can accurately understand and effectively handle customers requests, thanks to NLP technology. In this way they enable customers to interact with their bank via popular messaging tools, where chatbots are typically integrated. Moreover, several banks are also deploying voice assistants to automate interactions with the customers, while making them more natural. Voice assistants provide a conversational interface for customers to interact with their bank, which resembles popular voice assistants in smartphones (e.g., Siri) and smart homes (e.g., Alexa).


7. Personalized Financial Management

There are fintech firms that provide customers with digital assistants that help them manage and plan their finances. These tools fall in the realm of personalized financial management (PFM) applications. They operate by analyzing customers’ behaviour (e.g., incoming payments, credit card transactions) towards recommending them the best ways to reach certain financial goals such as investing a certain percentage of their income and ensuring that they will have the cashflows required to pay their bills. From a technological perspective, PFM software leverages a rich set of data management and data analytics technologies.


8. Brand and Reputation Management

There are fintech firms that help banks and financial institutions in managing their brand and reputation. This is based on the combination of a variety of advanced analytics and machine learning techniques, which are used to detect fraud, to extract and analyze customers’ sentiment about the brand, as well as to identifying abnormal and suspicious customer behaviors.


9. Customer Centric Analytics

Financial organizations have currently access to more data about their customers than ever before. For example, they can access and consolidate information about customers’ payments, loans, credit card transactions, investments and more. Moreover, the advent of Open Banking and Open APIs enables banks to enhance this information with additional data from other banks subject to the customer’s consent. The availability of these data provides a sound basis for developing accurate customer profiles. Accordingly, they offer to the customers a host of personalized products and services by means of customer-centered analytics. This process leads to more satisfied customers and increased revenues for financial organizations.


10. Fraud Detection

It is true that modern cybersecurity measures like two-factor authentication have led to a reduction of fraudulent incidents such as credit card fraud. Nevertheless, fraud detection remains largely rule based, which is a setback to identifying and mitigating complex forms of financial fraud like Anti-Money Laundering (AML) and capital markets manipulation. Many fintechs work in the direction of alleviating these limitations. To this end, they employ ML and statistical learning techniques to unveil hidden patterns of fraudulent behavior. Most importantly, they can do this accurately and in real-time.


The above list of use cases is representative, yet far from being complete. It portrays fintech’s disruptive potential in the finance sector. Despite the potential benefits, it must be also underlined that fintech innovation represents a threat to most businesses. This is because it brings a disruption of business models, ways of working and of closing sales processes, as well as a more intense and competitive environment. Therefore, banks should manage their fintech strategies through a multi-year roadmap, considering the market tipping point in order to effectively prepare for an uncertain future.

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