6 REASONS WHY AI AND FINTECH ARE TO BECOME BFFS

The word on the digital street is that “AI can transform the productivity and GDP potential of the global economy.” As far as the numbers go, we’re talking about the potential contribution of $15.7 trillion by 2030.

In their “2018 Digital Trends in Financial Services” report, Econsultancy and Adobe shared that 20% of financial institutions are already incorporating AI into their day-to-day and 41% hope that they will do so in the nearest future.

There’s no question that the competitive race to capitalize on AI’s unmatched capabilities is on! Here’s how we expect AI to reshape the Fintech market:

1) How AI Improves Customer Service & Personalization

AI Customer Service Fintech

We’ll witness many efforts to implement AI into customer experience, starting with chatbots and digital assistants. Digital assistants are a clever blend of Big Data, machine learning, and natural language processing (or NLP – the process of converting data into human language) enclosed in a conversational interface.

One of our recent surveys revealed that many banks are already experimenting with chatbots and virtual assistants, which will soon, hopefully, become financial advisors. Bank of America’s digital assistant, Erica, is a great example. It uses text messages and voice to provide clients with financial advice 24/7.

There’s lots of data analysis at work with different, less common customer queries getting examined in real time. The algorithms learn, which makes it possible to have ready-made answers without the need to turn to consult human experts every time.

The forces of Artificial Intelligence are also aimed at making customer service more personalized. Transactional and many other types of data can be used to connect with customers at different touchpoints and understand their preferences, improving the user experience.

There’s a company in London called Personetics that uses NLP to create conversational agents for different banks and other financial institutions. These agents work toward improving UX. Each conversational agent can be “taught” to respond in a certain way using customer emails, feedback forms, etcetera. The agents then use NLP to interpret customer inquiries and redirect them to a relevant page offering a particular service.

This allows financial institutions to solve issues for their customer quickly, maintain trust and promote user engagement. After all, such agents can help customers feel like they’ve accomplished something positive, and fast – which reinforces customer confidence.

2) Wealth Management and Artificial Intelligence

In this realm, AI acts more of as advisory support, helping wealth managers and advisors to make decisions, generate investment ideas, create portfolios with better investment options, and predict asset risk via data analysis.

Greg B. Davies, Head of Behavioral Science, at Oxford Risk says:

“When it comes to advice, we should be thinking about AI in terms of Iron Man, rather than the Terminator.”

What AI does is dives into a large pool of data and analyzes it to create highly specific, personalized strategies that wealth managers can then communicate to their clients, building stronger, long-lasting relationships.

Through such an advanced data analysis, Artificial Intelligence can easily identify new price signals, price volatility, and more successful investment options, making the most out of different data sets and market research. The data can later be utilized to forecast the future state of affairs on the market and even identify patterns and trends.

AI applications in wealth management can also be seen in robo-advisory. The below image is a great demonstration of how robo-advisors have evolved.

Robo-Advisory_evolution
Robo-Advisory Evolution: Digital Wealth Management from 1.0 to 4.0

[Image Source]

According to Deloitte, we’re in the era driven mostly by hybrid robo-advisors (3.0). That is, investment advisors use technology for a rebalancing of portfolios, asset allocation, and so on. You can even say that some steps have already been made to move to the 4.0 version.

Meet Amelia, IPSoft’s wealth management expert. Amelia can handle tasks at different stages of client lifecycle – from customer onboarding to investment transactions and recommendations for portfolios optimization. She can also utilize multiple data sources and back-end processes to give you different information on investments. She can analyze, learn and use her contextual awareness capabilities to give insights on various investment options, stock performance, and much more.

3) AI in Personal Finance

AI in Personal Finance

AI is finding its way to personal finance as well, helping users make sense of their financial plans, savings, and spending habits. This comes in handy, given the fact that around 49% of Americans reported that they spend more than they can reasonably afford.

How can you save money with Artificial Intelligence? There are several personal finance management (or PFM) software options to look at. Let’s start with Wallet.AI that analyzes your everyday spending activities (food you eat, places you shop, social media posts, online purchases) and identifies patterns, providing advice when necessary.

Acorns is another great example. With Acorns, you can link your credit and debit cards and then get a rounded-up value of your every purchase. Say you bought a T-shirt for $10.35. Acorns rounds it up to $11 and puts the difference to your share portfolio. It uses machine learning to classify your spending habits and determine how financially savvy you are, providing tailored insights based on this data analysis.

AI’s benefits in personal finance go beyond transactions, savings and spendings monitoring, though. You can find Artificial Intelligence being used in dividend management, transaction limit approaching notifications, and so on.

4) AI in Fraud Analytics

AI-in-Fraud-Analytics

Financial word giants have been utilizing AI to detect fraudulent activities for a while now, so, by no means, AI in fraud analytics is a new trend. As fraudsters become more advanced, though, we see financial institutions like CapitalOne ramping things up.

CapitalOne’s AI-powered fraud detection tools can now learn from the notifications they send to millions of their customers to recognize what types of transactions have a higher probability to be fraudulent.
Youssef Lahrech, a senior vice president at CapitalOne, further elaborated that the algorithms can even learn in more personalized ways. For example, Eno, CapitalOne’s virtual assistant, can warn customers regarding potential fraudulent activities via a conversation.

5) AI Driving FinTech in Developing Countries

In one of the recent blogs, we mentioned that emerging markets are getting stronger in FinTech – Artificial Intelligence is one of the reasons for it. Countries like China and India have been relying on FinTech quite heavily – over 50% of consumers in India and around 70% of consumers in China utilize FinTech to raise money or manage their finances.

Aside from that, developing countries have been capitalizing on AI in FinTech to help their financial institutions deal with their own limitations.

Artificial Intelligence has been useful in helping determine potential borrowers’ credit score – an analysis that’s challenging to do in emerging markets due to the differences in credit scoring models and the difficulties of tracking customer incomes. That’s where P2P lending options come into play. Such service leverages social media platforms to find out whether a potential borrower is creditworthy.

6) Automated Processes

Manual report generation, claims processes, and many more may well be off the chores list for financial institutions – which means saved time.

AI-powered technology leverages Natural Language Generation (or NLG) to help financial institutions automate reporting. The way it works is Artificial Intelligence uses different algorithms to generate natural language text from data and stores it in specific easily accessible databases. That’s how Yseop Compose operates, creating customized reports for its clients.

With AI, filing an insurance claim is easier. Transactional bots, like Swishbot, equipped with image recognition, fraud detection, and payout prediction algorithms, ensure that the process takes less time consuming and less prone to errors altogether. The bots accompany customers through the entire process, step by step. You can upload videos or images of the damage and let the bot analyze the data to come up with a range of payout values.

The wheels have only started to turn for AI in FinTech, and more groundbreaking developments are to come. It very well may be that the most fascinating, innovative movement you can be a member of is Artificial Intelligence. Time will tell us more!

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