This article discusses how integrating artificial intelligence in fintech apps can improve your business outcomes.
Let’s see how AI can improve a FinTech app. Consider the following benefits of artificial inetlligence in fintech sector:
1. Reduce operational costs
A basic premise of innovation in the world of business is that a business can offer the benefits of innovation to customers only if it manages the costs. FinTech offers convenience and high accessibility to customers. Traditional financial institutions couldn’t offer these since the cost was too high.
Financial institutions can offer superior services since they have been able to control their operational costs. Artificial intelligence in financial technology has played a key part here.
As you can read in “AI is accelerating the growth of FinTech companies”, AI algorithms have made a difference. Assessment algorithms help them to develop better financial products, moreover, predictive analytics can guide customers to choose the right product or service.
AI helps financial industry to better market their products. Intelligent automation driven by AI is helping FinTech companies to reduce costs at various stages of their operations.
2. Automate customer support
I have mentioned how AI is reducing operational costs for FinTech companies, and the automated customer support function is a key example of this. FinTech companies see three prominent patterns concerning customer service, which are as follows:
- Many customer queries and requests are similar, i.e., different customers may ask the same questions. AI algorithms with the help of analytics can address these questions, which can free up experienced customer service representatives to address more complex questions.
- Customers need instant responses, always. AI-powered chatbots can address many of these questions immediately, transferring only the specialized queries to the customer support team. Read more about this in “How Fintech companies can benefit from a chatbot?”.
- Given that FinTech companies attend to customers from different geographies, their customer-service costs would be very high if they depend on human beings completely. AI chatbots help to manage this cost.
Lydia, a well-known mobile payment app uses a chatbot for customer support.
Our blog here discusses how you can develop a smart chatbot for excellent customer experience.
3. AI-powered financial advisor
Like chatbots for customer service, AI-powered digital assistants can help users of FinTech apps by providing them with financial advice and personal financial management services.
These digital assistants can help users to make sense of their financial plans depending on their spending habits, bank statements, etc., which help users improve their asset management.
Such AI-powered advisors will use the NLP capabilities. They can use machine learning algorithms to study the patterns of a users’ transactions, moreover, they can use a product recommendation model.
As a result, these digital assistants can recommend financial products and services to users. Read more about this in “Ten applications of AI to Fintech”.
4. Proving credit-worthiness
Many people don’t have sufficient credit history, therefore, their credit score is low. As a result, banks don’t take the risk of lending money to them. Perfectly trustworthy people that are capable of paying back loans might not get loans due to this.
This is an area where FinTech firms with AI-powered solutions are helping customers. LenddoEFL, a Singapore-based FinTech industry start-up uses AI technology to analyze alternative data points to determine the credit-worthiness of a potential borrower.
Users that sign-up with LenddoEFL allow the app to mine their data from social media, web browsers, geo-locations, smartphones, etc. The AI algorithms of LenddoEFL analyze various aspects and determine the credit-worthiness, e.g.:
- If a user writes a detailed email subject line, then the user is likely detail-oriented.
- If LenddoEFL finds that a user regularly uses financial apps on his/her smartphone, then its algorithm determines that he/she is likely serious about financial matters.
Read “How artificial intelligence could replace credit scores and reshape how we get loans” to learn more about how LenddoEFL uses such unconventional data points to predict credit-worthiness.
5. Automating financial reporting
You can use AI to create financial reports from financial data. A Chicago, US-based company called “Narrative Science” has built a product named “Quill”, which utilizes “Natural Language Generation” (NLG), an AI capability.
Banks and financial firms have massive data, and they create reports out of it after detailed data analysis. Creating these reports requires an in-depth analysis of data, therefore, it takes time. Companies need such reports repeatedly, even though the input data sets could be different.
Quill uses analytics to derive insights from the financial data, subsequently, it uses NLG to create financial reports like analysts would do. Users of Quill can control the analytics, language, and format, moreover, they can customize the reports. Read more about this in “Artificial Intelligence use cases in FinTech”.
6. AI stock trading
A powerful use case of AI technology in financial sector is AI stock trading. Robot advisers can use AI algorithms to analyze a very large number of data points, and they can execute trades at an optimal price. AI trading systems can help analysts to forecast markets with better accuracy.
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Trading firms can use AI algorithms to mitigate risks more efficiently, moreover, they can deliver greater returns to their customers. ML and AI algorithms can process millions of data points and derive insights much faster than current systems that use various statistical models.
Read more about this in “How AI trading technology is making stock market investors smart – and richer”. Various companies have developed such solutions, e.g.:
- Chicago, USA-based Neurensic has developed an AI platform to identify complex trading patterns across many markets in real-time, and on a large scale.
- GreenKey Technologies, another Chicago, USA-based company has created an AI-based trading platform that uses speech recognition and NLP. The platform helps traders to quickly search through conversions and financial data.
7. More accurate fraud detection
Banks and financial services institutions operate under stringent regulations, and the focus on fraud detection, data security, etc. is heavy. Naturally, FinTech companies need to comply with the same regulations and demonstrate the same focus, as I have explained in “How to secure your Fintech app”.
The good news is that banks, financial services institutions, and FinTech companies indeed take fraud detection, security, etc. seriously! However, there is also an undesirable impact.
The information systems of these companies continuously scan transactions and detect frauds, using various pattern-matching techniques. However, overly secure systems sometimes identify perfectly valid transactions as fraudulent ones! This adversely impacts the user experience.
MasterCard, the American multinational financial services giant is taking steps to change this. Its AI-powered “Design Intelligence Platform” makes fraud detection more accurate, therefore, it will prevent valid transactions from being flagged as fraudulent ones.
MasterCard is consistently upgrading its AI capabilities towards this. In 2017, the company has acquired Brighterion, an AI start-up that makes information security systems smarter with its algorithms. Read more about this in this MasterCard press release.
8. Augment financial research using AI
Investors carry out plenty of financial research before they invest, and AI is helping them to reduce manual effort here. While even the most experienced financial experts can only process a limited amount of information available for their research, ML algorithms augment their capability significantly.
Not only do these AI/ML tools process a much larger data set, but they also help with due diligence. AI/ML sentiment analysis tools can review news, financial reviews, etc., and they provide insights into how the markets view the prospects of a company or securities.
AlphaSense, a New York, USA-based company offers an AI-powered search engine. It helps banks and financial services institutions to augment their financial research, as you can read in “AI and the bottom line: 15 examples of Artificial Intelligence in finance”.
9. Customer risk profiling using AI
Banks, financial service institutions, and FinTech companies need to profile their customers based on their risk score. This requires them to process a large amount of data.
“Artificial Neural Networks” (ANNs), a subset of AI capabilities can make a difference here. Artificial neural network are made of information-processing models that process information like our brain does as human intelligence, and you can read more about them in “An introduction to Artificial Neural Networks (with example)”.
AI systems comprised of ANNs can process a large set of customer data and use their in-built classification models. These models will then categorize customers into various risk categories, i.e., from low to high.
Banks can then associate financial products for a given risk profile. They can also predict potential defaulters and act accordingly, which helps them to lower their NPA levels. Read more about this in “Customer risk profiling using Machine Learning in lending”.
Finally, read more about real-life examples of artificial intelligence to understand how to leverage the potential of this advanced tech.
Planning to augment your FinTech app with the help of AI?
As you just saw, AI can significantly enhance your FinTech app. However, artificial intelligence in fintech development context can be complex, and you should engage a competent and trustworthy software development company for such projects.
We at DevTeam.Space have just the right track record for this, and you can judge our capabilities by reading “AI development life cycle: Explained”.
If you wish to engage with competent software developers at DevTeam.Space, get in touch by filling this quick form with your initial AI requirements for finance industry project.
Frequently Asked Questions
You will need to onboard developers or a development team that has expertise in AI development. You will also need to outline exactly what you want the AI to do and instruct your development team to ensure those goals are met.
AI is used in most areas of the financial world. When combined with big data analytics, just a few examples of AI in fintech include digital transactions, stock trading, wealth management, underwriting process automation, smart contracts to detect fraud, smart wallets, anti-money laundering practices, and also in marketing and personalized financial advice for increased customer satisfaction.
AI can be applied to many conventional business practices, as such it can revolutionize companies and make them far more efficient and effective.