UncategorizedAI Revolution in the Fintech Industry

INTRODUCTION

India has witnessed a remarkable shift in its payment landscape in the last decade.  Along with the boost in digital economy and proliferation of smart phones acting as catalysts, the traditional payment methods are gradually being replaced with more convenient and secure digital alternatives. Digital payments are transactions that are made through digital or online mode with no physical exchange of hard cash involved where the payer and payee use electronic medium to exchange the money.

There are various methods to utilise this alternative digital transaction. It can be through banking cards (debit/credit or pre-paid cards), Aadhaar Enabled Payment System, Unified Payment Interface (hereinafter referred to as “UPI”), Mobile Wallets, Internet Banking etc. The adoption of UPI in India has promoted numerous fintech companies and has created a streamlined digital ecosystem in India. Moreover, this adoption has proven to be most crucial during the COVID-19 pandemic. The sudden need for contactless transactions has led to the surge in the usage of various fintech applications.

Reserve Bank of India (hereinafter referred to as “RBI“) released the National Strategy for Financial Inclusion 2019-2024. It set the vision and objectives of financial inclusion policies that can ensure easy financial access and timely credit for low-income and vulnerable groups within the Indian population. This national financial inclusion strategy will complement the RBI’s move to issue differentiated banking licenses, all in an effort to bridge existing gaps in ensuring universal access to financial services. That said, the use of innovation and technology continues to be an important pillar in this journey and artificial intelligence (hereinafter referred to as “AI“) technology has emerged as the leading facilitator of financial inclusion.

For example: Banks offer apps that can only be accessed with face or fingerprint recognition. This is primarily made possible by artificial intelligence. Ocrolus, a New York based company offers document processing software that combines machine learning with human verification. The software allows business, organizations and individuals to increase speed and accuracy when analyzing financial documents. Ocrolus software analyses bank statements, pay stubs, tax documents, mortgage forms, invoices and more to determine loan eligibility, with areas of focus including mortgage lending, business lending, consumer lending, credit scoring and KYC.

AI has emerged as a game changer, transforming how we manage, invest and interact with the finances. The amalgamation of AI and Fintech has introduced ground-breaking capabilities that were once confined to the realm of science fiction.

AI TRENDS IN THE FINTECH INDUSTRY

To facilitate one’s navigation through the world of AI in fintech, here is the brief list of latest AI trends in the industry. These can be utilised by the fintech platforms to gain a competitive edge over others.

1. Natural Language Processing – It is subfield of AI that enables computers to comprehend human languages. Fintech companies use natural language processing to create chatbots and virtual assistants capable of engaging with customers in order to address their queries.

2. Machine Learning Algorithms – This is used for credit scoring and fraud detection. Machine learning algorithms analyse large amounts of data to identify patterns and make predictions based on customer behaviour. This is especially helpful in lending institutions where due diligence and credit score are significant to proceed with a borrower’s application.

3. Robotic Process Automation – They facilitate in faster and more efficient back-office operations by using software bots to automate repetitive tasks, including data entry and reconciliation. This helps the fintech companies to decrease errors and enable their staff to concentrate on more high value tasks.

4. Big data Analytics – This is a powerful tool to enhance risk management and investment decision making. Fintech industries utilise this to analyse vast amounts of data from numerous sources, including social media, to gain insights into the market trends and customer behaviour. This assists them in making well informed decisions regarding investments.

5. Digital Wallets– Digital wallets have emerged as a transformative force in fintech, fundamentally changing how we invest and manage money. Digital wallets like Apple pay, Google Pay, PayPal etc. allow the user to store payment information and complete transactions on your mobile device itself. They securely store the user’s credit/debit card details, billing address and account passwords. This has become a convenient way to pay while also incorporating additional security measures like finger print authentication or facial recognition.

China has  incorporated AI in  digital wallets. It has built an entirely new payment system based on digital wallets and QR Codes that runs through their own big tech firms: “Alipay” running through “Alibaba” which is an online shopping platform) and “WeChat Pay” running through Tencent which is a social media platform.

This has created an alternative payment ecosystem with different incentives between merchants, consumers and payment system providers. Chinese consumers can pay everything from utility bills to street food using digital wallets linked to their bank accounts.

ROLE OF AI IN FINTECH INDUSTRY

AI’s multifaceted role has been paramount in shaping the future of financial services. The numerous roles played by AI are as follows:

i. Automatizing Financial Processes- The fintech sector has undergone a paradigm shift with the integration of AI, automating various financial processes. Through advanced algorithms and machine learning, AI systems streamline operations, enhancing efficiency and diminishing manual labour.

AI helps in analysing the vast pool of customer data in order to efficiently execute trades, oversee investments and optimize portfolios. This not only enhances decision-making processes but also reduces human errors. Through the utilization of AI algorithms, transactions are processed in real-time, ensuring prompt settlements and diminishing the likelihood of payment delays.

ii. Enhancing the Customer Experience- Companies collect vast amount of customer data, analyse it using AI powered systems in order to personalize interactions, predict customer behaviour and provide tailored recommendations. Fintech companies employ chatbots and virtual assistants to resolve customer queries and issues promptly. Also, the AI powered communication tools enhance engagement through targeted messages and alerts, keeping customers informed and satisfied.

• For example, Bank of America’s AI powered chatbot Erica, can help customers manage their finances and make payments.
• Also, Capital One, a financial services company, has integrated its chatbot “Eno” with Amazon’s Alexa to provide customers with voice enabled access to their accounts.

iii. Fraud Detection – AI can automatically detect fraudulent transactions by spotting discrepancies and anomalies. It recognizes patterns of behaviour that deviate from the norm, immediately raising red flags. This becomes significant in the world of finance where security and trust are paramount. With the rapid change in technology and constant evolution of fraud tactics, AI provides dynamic and adaptable solution.

• For example, MasterCard has developed AI-powered fraud detection systems that can identify potential fraudulent activities in real time. Their system analyses large volumes of data, identifies patterns, and compares them to historical data to detect any suspicious activities.

iv. Regulatory Compliance – AI plays a pivotal role in ensuring regulatory compliance in the fintech industry. It assists the companies in monitoring transactions, detecting suspicious activities and reporting them to authorities. Further AI also assists with Know Your Customer (hereinafter referred to as “KYC”) processes and anti-money laundering (hereinafter referred to as “AML”) compliance.

– For example, ComplyAdvantage, a Regulator Technology (hereinafter referred to as “Regtech”) company based in UK, offers AI-driven solutions for AML and KYC compliance. This platform helps companies meet regulatory requirements by identifying high risk customers and transactions.

v. Credit Scoring – AI analyses past credit histories, income, demographics and other financial factors to accurately predict a borrower’s likelihood on defaulting on a loan. This allows fintech companies to make informed decisions on whether to approve the loan or not.

REGULATORY FRAMEWORK

1. The Financial Crimes Enforcement Network

The U.S. Department of the Treasury established the Financial Crimes Enforcement Network in 1990 to provide a Government-wide multisource financial intelligence and analysis network. The Financial Crimes Enforcement Network (hereinafter referred to as “FinCEN”) collects, analyses and disseminates financial intelligence to combat money laundering and terrorist financing and promote national security, and prescribes rules for financial institutions’ AML compliance programmes.

FinCEN’s Innovation Initiative promotes the innovation in AML compliance through the adoption of new and advanced technologies.  The mission of the FinCEN is to safeguard the financial system from illicit use, combat money laundering and its related crimes including terrorism, and promote national security through the strategic use of financial authorities and the collection, analysis, and dissemination of financial intelligence.

2. Artificial Intelligence Risk Management Framework (AI RMF 1.0)

On January 26, 2023, the National Institute of Standards and Technology (hereinafter referred to as “NIST”) released the first version of its Artificial Intelligence Risk Management Framework (hereinafter referred to as “AI RMF 1.0”) which is also known as NIST AI Framework. This framework provides a structured approach to identify, assess, and mitigate the risks associated with AI systems.

This framework enables organizations to navigate the complicated world of AI technology while assuring ethical AI adoption, protecting against potential harm, encouraging accountability and transparency when implementing AI, and protecting individuals’ rights.

On March 30, 2023, NIST launched the Trustworthy and Responsible AI Resource Center, which will facilitate implementation of, and international alignment with, the AI RMF.

LIMITATIONS OF AI IN FINTECH

1. Data Privacy and Security – AI involves gathering and storing large amounts of data in order to train algorithms so that they make informed decisions. However, the collection of such data raises questions about consent, transparency, and the potential for unauthorised access. Customers may unintentionally relinquish control over their personal data when engaging with AI driven financial services, leading to vulnerabilities and exposure to privacy breaches.

2. Bias in AI algorithms – AI systems learn from the data fed into them. If the data itself is biased, AI will inherit those biases leading to skewed results. For example, an AI which is trained on biased lending data might unfairly deny loans to certain demographics.

3. Regulatory Challenges – As the digital technologies are taking over, Governments across the world are passing laws to protect the sensitive and personal information of their citizens. The providers of the financial information must ensure that their AI complies with all the rules and regulations in order to avoid any breach.

4. Job Displacement– AI performs most tasks faster and better than human workers. As this reliance on AI is increasing day by day, it may lead to job displacement in the fintech sector. It is crucial to consider the impact on human employment due to this development as well as to explore ways in order to upskill employees in the AI era.

5. Lack of Transparency – AI algorithms especially those based on deep learning are often referred to as “black box” due to their lack of transparency. It is often difficult to understand how these algorithms arrive at their decision, which can lead to data breach issues in a regulatory context. In case of black box algorithms even the developer of the AI is unable to comprehend as to how and on which input the AI has generated a particular result.

6. Customer Trust and Acceptance – People are often very cautious about investing their money and have second thoughts about handing vital financial transactions to a system that even professionals are not able to comprehend completely till date. The fact that these systems work on minimal human intervention, some customers believe that the AI driven system may not work in their best interests.

AI IN FINTECH: LEADING EXAMPLES

Fintech startups around the world are using AI to enhance their platform for better services and experience. Here are few examples –

a. Kasisto – Kasisto is a US based company which creates conversational AIs that can understand vague and ambiguous queries of customers and answer them reliably. It uses natural language processing for customer banking, business banking and investment management. Companies like J.P. Morgan, Emirates NBD, and Westpac are using Kasisto.

 b. Zest AI– They help financial risk providers carry out better risk profiling and credit modelling. It is US based company which builds models to spot the riskiest borrowers and swap them for more creditworthy applicants. They use machine learning to increase approval rates, cut credit losses and improve underwriting processes. It has developed Zest Automated Machine Learning Platform that helps businesses in assessing borrowers with little to no credit information or history.

 c. SESAMm– It is a France origin company which specializes in big data and artificial intelligence for investment providing organization with the ability to make timely decisions by tracking ESG, risk controversies and positive events. SESAMm has developed its own off the shelf platform “TextReveal” which empowers users to generate AI insights from web data on millions of companies in less than a minute.

d. Adyen – This is a Dutch based company which acts as a payment processor, point of sale app, card issuer, analytics platform and a digital bank. It uses AI in revenue optimization, customer insights, risk management etc. Their clientele includes Uber, Spotify, Ebay, Booking.com and McDonalds etc. The platform has united everything at one place for customer convenience.

e. Perfios – This is an Indian company which offers a powerful data analytics platform that is mostly used by banks and non-bank financial companies. This helps prevent fraud and make better lending decisions and manage assets. Banks like Axis Bank and AU Small Finance Bank, have used Perfios.

AMLEGALS REMARKS 

The intersection between AI and fintech not only offers a myriad of new possibilities but also challenges and ethical considerations that must be effectively addressed. Fintech companies are increasingly harnessing the power of AI to automate workflows, improve decision making, and add value to operational efficiency. They can stay ahead of the competition and guarentee that their services hold the highest standards of quality by implementing AI in quality management.

The success stories of a few companies mentioned above reflect how AI has become an elemental aspect in assisting fintech companies in their day-to-day operations. The penetration of AI in the field of financial technology is bound to increase more than ever in the coming decades.

A balanced approach must be formulated that takes into consideration innovation, data security and regulatory compliance. The world leaders all around the world must be at the forefront of policy making so that citizens are protected from the overreliance and implementation of AI in every sector, including fintech.

-Team AMLEGALS assisted by Ms. Surbhi Talreja (Intern)

For any query or feedback, please feel free to get in touch with mridusha.guha@amlegals.com or liza.vanjani@amlegals.com.