Why is the Fintech industry turning to AI for new prospects?

What does the financial sector require to match up the business dynamics of Fintech companies? A more customer-friendly financial service, transparent transaction fees, advanced security practices, cost optimization, and real-time data integration. Since Fintech is all about incredible convenience, innovations, flexibility, cheaper financial services, and ease in the accessibility in the finance ecosystem, the adoption rate of fintech has increased.

Financial inclusion that would enable customers to leverage benefits from the financial system is the ultimate goal of FinTechs. Fintech companies are under constant pressure to search for new opportunities to provide better financial services to the customers. But, providing the customers with a breed of tech-oriented financial services wasn’t enough in a market where banks were dominant. They lack the popularity, trust, and capital that banks had. So they turned to what they had at their disposal, data, a competitive resource. 

They started analyzing data from browser specifications, transaction histories, images, voices, other unstructured data, data through applications, and geolocation. But Fintechs weren’t any more limited to legacy systems, pivot tables, and spreadsheets for data analysis. They found out the key ingredient that would create an impact in the financial sector by solving the challenges and complexities that people had faced with banks.

 Yes! You guessed right!

Artificial intelligence is solely responsible for innovations in data analytics and product development in FinTech and the finance industry is driven by potential opportunities that help it to grow. So, it is worth investing in such an impactful technology in FinTech. 

With AI, customers can reap the benefits of easy and cost-effective financial services. By leveraging the power of AI, FinTech companies have tech solutions that resolve all the complexities and difficulties of the customers, right from borrowing loans to money invested in other profitable options. Moreover, AI has the capacity to control costs at every stage. Therefore, FinTechs can offer high-quality financial services to its customers using a lean workforce because of the incredible compatibility between a man and machines. 

How Artificial Intelligence will help FinTechs to grow?

It is the responsibility of AI to foster the growth of FinTechs through its tremendous capabilities in data analytics and management. Let us explore the cases where AI has given the opportunity to drive innovations into FinTech.

1. Decision-making

AI has a tremendous capacity for accurate decision-making. These decisions are data-driven and are made at lower costs. Every marketing decision comes up with some complexities. To jump to some decision, one has to understand and know what are the customer requirements and desires and make sure that the decisions are aligned to these needs and desires. 

Another important factor is to analyze the ever-changing consumer behavior for carrying out the best marketing decisions. 

AI helps to get in-depth insights into the buyer personas through AI modeling and simulation. Moreover, these techniques can also be used for consumer behavior prediction. AI is used to support the decision system through data collection, forecasting, and analysis in real-time. 

AI helps in customer relationship management as well. It provides a prediction of the customer’s value for a lifetime through AI’s buyer persona model. Moreover, AI within CRM provides automated functions like managing contacts, recording & analyzing data, and prioritizing leads. 

AI can be used to craft content that targets the audiences with the help of learned information through the user’s content preferences. 

2. Customer Support

Nowadays people rely more on messaging apps for communicating and marketing their brands. Messaging apps have become one of the popular customer engaging mediums. Therefore, businesses have an incredible opportunity to connect with new and existing customers by leveraging AI-powered, fully tailored, real-time messaging bot services.

Facebook Messenger is one of the most popular examples of AI-powered chatbots. It leverages chatbot technology with cognitive capabilities. This idea is now used by many industries namely fashion, food and supply chains, airline, travel, eCommerce, hotels, to name a few. Consumers are enjoying this interaction as booking tickets, hotel accommodation, travel trip has become handy with the help of a bot.

In Fintech, AI helps in making interfaces more interactive, intuitive, and economical. It will help to align the solutions to the specific customer needs by analyzing and learning from their browsing history, previous conversations, and preferences. FinTech leverages AI and builds an integrated system that helps in:

• Identifying and resolving customer issues
• Gathering information
• Processing and acquiring knowledge from the collected information.
• Identifying the behavior pattern of the customer.
• Analysis of their decisions and preferences
• Creating solutions and products that align with their requirements
• Prompting alert messages
• Prompts fully tailored offers and discounts
• Real-time support in the form of blogs, FAQs, chats, reports, etc.
• User-friendly customer experience
• Automated responses reducing cost and time.

Fintechs are using AI to automate responses due to its independence from holidays and time limitations, thus giving customers options to resolve their issues whenever they want. So you can think of uninterrupted customer service for your financial institution that would help your organization to address and respond round the clock to the inquiries. This would result in:

• 0 waiting time
• Fast and high-graded tailored solutions for the problems
• Incredible customer satisfaction
• Commitment level is highly improved
• Higher-engagement
• The reputation of the brand has increased.

3. Detecting Frauds 

Fintechs, especially businesses that are into transactions and payments are subjected to the risk of frauds that may cost them billions of dollars. One of the biggest challenges in fraud detection through legacy systems is that they are slow and complicated. They are often designed as rule-based systems which include:

• Long processing
• Numerous verification steps
• Much manual work to go through all detection rules
• Reacts only to a certain number of potential red flags

The current scenario with fraud is that it is evolving day by day. Fraudsters are coming up with new methods to capture the information and steal the money. This is the reason why people are investing in AI and answering these threats through machine learning. 

The advantage of having a machine learning-based fraud detection system is as follows: 

• Identifying data correlations that are hidden and implicit.
• Fraud scenarios are automatically detected.
• It provides a much-reduced number of verification steps as compared to rule-based systems.
• It provides real-time processing
• Anomaly detection where any kind of anomaly behavior is detected while machine learning analyzes the incoming data say any transaction, payment requests or application data. 

Machine-learning fraud detection comes up with a lot of solutions but you have to be prepared with the mindset to spend a lot of money. There are multiple vendors that offer a wide variety of fraud detection services with various pricing structures and compatibility specifications.

4. Credit Risk Scoring

Risk management has always been the top spot in the BFSI sector, although AI and ML have a wide range of use cases in this area. AI & ML are capable enough to add value to the credit value chain, right from the underwriting process to measuring and analyzing risks. 

Banks usually come across a diverse range of borrowers. Each borrower has a profile that holds risk and exposure parameters. To simplify this concept, maximum borrowers are non-defaulting individuals whereas a very small set of borrowers will fall under the defaulted category. Now, if the banks follow the traditional analytical model, a defaulted borrower moving to the credit dataset would cause an imbalance and skewed results. This will further cause inaccuracy in the predictive insights, that in turn, results in degraded performance. This will cost the banks to lose viable business opportunities. 

To resolve this issue, Machine Learning models would build individual clusters of datasets. Various merging methodologies will be applied to these distinct clusters and figured out whether the loan can be offered to the customer. Unlike traditional methods, ML creates clusters and tries to merge them for creating a diversified dataset that reflects on the real scenario. 

Also, there are a lot of investments in terms of time, money, and expertise when it comes to assigning a credit rating to a commercial entity. The banks assign this work to some credit rating agencies. The agency conducts a thorough evaluation based on financial and non-financial parameters. But this isn’t the case always. Investing in agencies for companies that are very new or are budding technology establishments isn’t suitable. Thus, banks again miss out on the opportunity to capture this segment as they are denied with the capital they require for escalation. 

To make up a risk profile of a company, AI and ML are capable of capturing their transaction patterns. Taking into account the subjective parameters like market resilience, change management, currency flux, ethical stance, and so on, are all used by AI to build a robust risk model that will be more accessible. 

AI is also beneficial for underwriting. The manual reviewing of applications is ignored completely due to advances in credit risk management where the lower credit risk applications can be automatically accepted, saving time and resources.

5. Insurance Management

Do you agree?

Data is generated every second. We need to pace up with this data generation speed. Our need to extract insightful data out of such huge heap demands for a model that is both powerful and innovative. Thus, the need for cognitive technology comes into the picture. 

When we say ‘huge heaps of data,’ the insurance industry itself sums up to this phrase. Every day, the insurance domain is loaded with thousands of claims, inquiries, and other data which itself makes it eligible for utilizing AI and cognitive technologies. The insurance sector is heavily penetrated by disruptive technologies like AI for its tremendous capacity from claim processing to enhanced customer services. 

Claim management itself includes numerous steps like input form, documentation about the incident, medical reports, estimates of the repairs, and so on. These steps make claim management a tedious task. Since the entire decision-making and validating process depends on these steps, evaluation has to be carried out to avoid any kind of inaccuracy and fraud. 

Machine learning can be used for managing claims at different stages. Insurers can automate the entire handling mechanism of a huge amount of data in a limited period of time by leveraging AI. It is also capable to enhance customer experience by fastening some of the claim processing, which in turn, reduces the overall time for processing and cuts down the costs.

With the AI-based claim management system, the insurers can track their claims faster. This helps in reducing time and processing costs through RPA. Also, fraudulent claims can be identified due to AI’s capability of identifying patterns in the data. Thus, insurers can make a fast decision about their claims. 

With the help of Machine Learning, damage evaluation and cost prediction from historical data are done automatically with AI systems that can learn and discover new cases. Also, the insurance industry has employed AI-powered chatbots to provide customers with efficient, fast, and round the clock customer service. 

6. Virtual Financial Assistants

Now making financial decisions won’t be a hustle and bustle for you. Thanks to the automated virtual financial assistants and planners. They will walk hand in hand with you right from events monitoring, trending prices for bonds and stocks catering to your profile’s requirements to managing your expenses and advising on your savings. They will provide you with tailored recommendations about buying or selling stocks and bonds. These advisors, known as ‘Robo-Advisors’ are on high-demand and are offered by various Fintech startups and financial companies in the BFSI sector.

7. Wealth Management

Are you wondering how AI can help in wealth management for the masses?

By using AI for wealth management, wealth advisors witness a reduction in cost and time for monitoring and tracking regulatory risks. AI is capable of taking into account the client’s preferences, financial trading trends, associated regulations and provide wealth managers in-depth insights and recommendations that even human analysts can’t match up. 

Another benefit of using AI-powered solutions for wealth management is their capacity to provide accurate and personalized insights through learning from clients’ feedback and behavioral patterns in the client data. 

In wealth management, AI has created an impact in the following fields:

Personalization: AI is known for its personalization capabilities where the financial advisors are helped to identify the client’s investment preferences and provide the client with a fully tailored advice that matches up to his goal and profile risks.

Engagement: AI has provided a greater source of engagement helping the wealth advisors communicate the personalized insights based on the client’s time and preferred channel. 

In a nutshell

Adopting AI models in your fintech applications is worth investing because:

• The demand for fintech applications is on the rise. They are able to provide secure, fast, and reliable transaction facilities in the finance ecosystem. 
• AI models help in meeting the demands of customers and the market.
• It has the capability of providing data-driven operations.
• AI provides cost-effective and tailored solutions to consumers. 
• AI comes with a complete set of operations that helps in fraud identification and prevention
• It provides real-time data processing and customer support.
• Improvised customer experience through cognitive and sentiment analytics.
• Enhanced customer experience by improving pre and post-sales services. 
• With AI, you are guaranteed to get solutions for complex problems.
• Solutions are simple and easily accessible by masses. 
• A set of financial services and solutions that are beyond the core offerings. 

With such advancement in your financial services, you can imagine how AI technology will bring an incredible pool of opportunities for your business and boost growth with the help of FinTech software. You can partner with some of the leading FinTech development companies that will help you to achieve your goals with AI-powered solutions.