“Alexa, tell Capital One to pay my credit card bill.”
“Alexa, transfer $50 to Ryan.”
Thanks to Artificial Intelligence(AI) and Machine Learning(ML) capabilities that have brought the FinTech revolution, money transfers and payments have become this simple and seamless today.
From telling you where to invest your money to providing you personalized ways to manage your finances, and taking user security a notch higher, emerging technologies are shaping the FinTech industry beautifully.
The FinTech sector has never been left behind in adopting emerging business models and technologies. Both new and old players in the sector are taking the advantage of modern technologies, such as, Artificial Intelligence, Machine Learning, Internet of Things (IoT), and Blockchain, among others.
This post is based on the latest learnings and role of AI in the FinTech sector and is meant for both tech experts and key business stakeholders.
Here are the most recent use cases of Machine Learning and Artificial Intelligence (AI) techniques in the FinTech industry:
1. Data security
A recent research report suggests that 98% of the top 100 global fintech startups are vulnerable to major cyberattacks, including phishing, malware, app security attacks across mobile, web, and other platforms.
Data security is a critical area, especially when it comes to the banking and finance sector.
Maintaining data security is a challenge for organizations in this world where malware and data breaches have become so common.
Now that financial enterprises have moved their services to digital channels, they can collect and analyze massive amounts of user data.
However, collecting data is one thing and maintaining its security is another.
AI and ML applications are proving to be a boon for the FinTech industry, allowing it to take data security a notch higher with the help of biometric technology like voice recognition, facial recognition, etc. These are a lot more secure than conventional PINs and screen patterns.
Some of the leading companies that invest heavily in AI and ML for data security purposes are PayPal, Stripe, and Skrill.
2. Fraud detection
According to Cybersecurity Data for 2020 published by Fintech News, phishing attempts rose 600% since the end of February 2020.
Do you know why fraudulent schemes are most common in the FinTech industry?
Well, it is because the opportunities to exploit weaknesses in data protection are maximum here.
With identity theft and phishing being the most common types of fraudulent schemes that are deployed today, FinTech enterprises are realizing the importance of being equipped with fraud prevention techniques.
The role of AI in FinTech is incredible when it comes to fraud prevention.
According to most research studies, the recovery cost borne for a fraudulent transaction is almost double the amount of money lost to it. For instance, for losing $1 to a fraudulent transaction, you are likely to bear over $2 to recover it.
According to a fraud predictions study for 2021 in The FinTech Times, card fraud scams will become the leading fraud type globally and the losses from these will rise dramatically, making it vital for FinTech enterprises to rely on AI and ML capabilities for fraud detection and prevention.
From small and medium FinTech enterprises to large scale multinationals, businesses in the sector are using advanced AI and ML algorithms and tools to detect transactional frauds with high accuracy, analyze millions of data points, identify suspicious account behavior, prevent fraud in real-time, and do a lot more to secure transactions.
It is through advanced ML capabilities that PayPal, the reputed online payments system company, avoids money laundering cases and differentiates fraudulent transactions from legit ones.
It is time to equip your financial services business with ML-powered monitoring systems, algorithms training, validation, and backtesting trained on historical payments data, to detect fraudulent transactions in real-time.
A reputed FinTech software development company can equip you with the latest AI and ML-driven technologies.
3. Risk Management
According to Cybersecurity Data for 2020 published by Fintech News, Coronavirus blamed for a 238% rise in attacks on banks.
From professional liability risks to regulatory exposures, theft of funds, data breaches, cyber events, and technology failures, Fintech businesses have a unique combination of evolving risks to manage.
According to a post published by Threats Mark, The Android malware “Gustuff banking Trojan”, which was seen in the first half of 2019, is not only targeting over 100 classic financial institutions, but also various services such as PayPal or Revolut.
With this figure, you can easily imagine the direction and rising trends in malware creation.
Lowering these evolving risks is not easy, especially with conventional methods.
This is where AI and ML techniques come into the picture.
By analyzing a huge volume of data, they can reduce risk levels significantly. A leading FinTech software development company can help equip your financial institution with AI and ML-driven technologies to manage and reduce risks.
For instance, banks can harness AI and ML algorithms to assess posed by customers applying for loans and make intelligent decisions based on it.
ML and AI help FinTech companies and banks to analyze customers applying for a loan, assign risk scores to them, predict customers who are at risk for defaulting on their loans, and adjust terms for each customer.
4. Process Automation
According to a FinTech Trends 2021 post on Medium, companies that use Robotic Process Automation(RPA) for banking tasks see a return on investment (ROI) of 100% within 3 to 8 months.
You might point out that startup costs for RPA are hefty.
Sure, but the ROI of automating banking and finance processes is truly phenomenal since it allows organizations to shift their focus on customer needs rather than do all the tedious work.
The post also estimates that AI will reduce bank operating costs by up to 22% around 2030.
Do you know what that means?
It translates to a savings of up to $1 trillion over the next decade.
Whoa!
That is big.
The FinTech industry is data intensive. The need for financial services providers to look at data in a smarter and more responsible manner is crucial.
Through ML-powered intelligent RPA solutions, FinTech software development companies have allowed them to automate their manual and most tedious tasks and look at data in the smartest way.
Chatbots and virtual assistants are popular process automation examples that work on AI and ML capabilities and improve customer experience and reduce costs.
Want to see an excellent example of AI in FinTech?
Wells Fargo & Company is a real-life example of a financial services company that uses an ML and AI-driven Facebook messenger chatbot to help users get information regarding their accounts and passwords.
It is high time for finance companies to consider replacing manual work by automating mundane tasks through ML and AI-driven intelligent process automation. A FinTech software development company with advanced ML and AL capabilities should be able to help you.
This year, we’re expected to witness more financial institutions adopting RPA to handle different backend tasks like security checks, account maintenance and closing, trial balancing, and credit card processing, among others.
5. Debt Collection
Even if you are a highly automated organization, you would agree that debt collection is still one of the slowest processes. Debt collection can become a huge problem if risks are not assessed properly. After all, lending is an extremely risky business.
Some of the challenges faced in debt recovery include balancing efficiency with customer experience, one-size-fits-all communication methods, and abuse of debt collection practices.
With global consumer debt rising dramatically and the collection success rate ((in most geographical regions) dropping lower with each day, the lending and debt recovery business is heading into difficult and uncertain times.
It is more important than ever for financial services to take appropriate steps to make their debt recovery successful and more effective.
With ML and AI capabilities, you can make your debt recovery more effective and fast by analyzing past customer behavior and assessing risks.
How does AI help FinTechs in debt recovery?
AI and ML find application in four main areas in debt recovery, namely, prediction of customer delinquency, segmentation of borrower risk, personalized communication, and tailored recovery/settlement proposals.
Data Security
The role of ML and AI in FinTech is phenomenal. It has proved to be a boon for the FinTech industry, allowing it to level up its data security game with the help of biometric technology which is a lot more secure than conventional PINs and screen patterns.
Fraud Detection
Using advanced AI and ML algorithms and tools, FinTech enterprises can detect transactional frauds with high accuracy, analyze millions of data points, identify suspicious account behavior, prevent fraud in real-time, and do a lot more to secure their transactions.
Risk Management
Modern ML and AI help FinTech businesses to analyze a huge volume of data, thereby reducing risk levels significantly.
Process Automation
With AI and ML-driven intelligent RPA, banks and financial services providers can handle different back-end tasks like security checks, account maintenance and closing, trial balancing, and cre`dit card processing, among others.
Debt Collection
AI and ML find application in four main areas in debt recovery, namely, prediction of customer delinquency, segmentation of borrower risk, personalized communication, and tailored recovery/settlement proposals.
Wrap Up: ML and AI in FinTech
In the modern era, financial enterprises are embracing the advancements in technology to improve the way they function. ML and AI technologies in Fintechs have allowed us to reduce risks and be more efficient in the way they function.
Jellyfish Technologies is a reputed software development company based out of Salt Lake City that can help you implement these disruptive AI and ML-driven technologies in your enterprise.
