Artificial Intelligence (AI) is currently a hot topic. Media coverage and public discussion about AI is almost impossible to avoid. It has so many use cases like self-driving cars, chatbots, predictive analytics, cashier-less checkout, and many more. According to a McKinsey survey, 56% of organizations are using AI in at least one business function.
The fintech industry has been benefiting from technologies such as machine learning (ML), artificial intelligence (AI), neural networks, and big data analytics for at least two decades. These technologies help fintech companies maintain personal contact with their customers, automate their customer support, enhance fraud detection, and facilitate decision-making. There may even be additional benefits as well. This blog post covers what fintech companies have to gain from implementing AI.
The concept of Artificial Intelligence has been around for many years, even going back to Greek mythology with stories about a mechanical man with human-like actions. As our knowledge of how the human brain works has expanded, engineers have attempted to teach machines how to act in more human ways.
This has led to the appearance of self-driving cars, smart assistants, robots, and many more innovations that streamline business processes in various areas ranging from marketing to health care.
Naturally, AI and ML have also found a place in the fintech industry (and for AI, more specifically in fintech data), and their utilization is expected to grow. Here are some stats provided by Statista.
Al investment in fintech is expected to reach 26.67 billion USD by 2026, with a CAGR of 23.17%
79% of the successful banking interactions happened through a chatbot.
Al-powered chatbots will help the fintech industry save $7.3 billion by 2023.
Use of AI in insurance claims will save companies $1.2 billion by 2023.
31% of the leading financial firms today use Al fintech software in their organizations.
Here is a list of sectors that AI in fintech will disrupt the most.
When you start reading about artificial intelligence (AI), several other closely related topics may appear in your reading list. These include machine learning (ML) and computer science. People often mistakenly use AI and ML as synonyms, but this is not correct. Let’s take a closer look at the two technologies.
Artificial intelligence makes it possible for computers to see, think, and act like humans. It doesn’t require these actions to be pre-programmed and is able to act independently.
Machine learning, however, refers to the ability to extract knowledge from data and to make predictions and decisions based on it. ML provides machines with the ability to learn new information and patterns without a developer’s guidance.
Machine learning can be described as a subfield of AI, which itself is a subfield of computer science. Data science, robotics, and deep learning are also subfields of computer science also used in software development, but if we bring them up as well, it will be too much information for a short blog post.
|Artificial Intelligence||Machine Learning|
|Enables machines to act like a human||Allows computers to learn from datasets provided|
|Creates a large system that can solve multiple tasks||Solves specific problems|
|Use cases include chatbots, virtual shopping assistants, and self-driving cars||Use cases include search and recommendation algorithms and process automation|
In the fintech industry, better results are achieved through synergistic applications of both concepts. Some of the most frequent use cases of AI and ML include the following.
Reducing costs: 38%
Gaining insight into customers: 37%
Improving the customer experience: 34%
Automating internal processes: 30%
Detecting fraud: 27%
Improving customer satisfaction: 26%
Next, let’s look at the details of specific use cases for AI in payments and fintech.
Financial fraud is one of the major concerns in the banking industry. Nowadays, due to how widespread digital technology has become, companies spend more and more on cybersecurity. According to a report by Deloitte, the average business invests between 6% and 14% of its annual IT budget into cybersecurity. Artificial intelligence and machine learning help financial institutions detect and predict threats and fraudulent users rapidly and effectively.
No more waiting for an operator in a traditional call center. Financial companies are now able to maintain personal contact with their customers via their devices. AI-powered chatbots automate repetitive tasks such as collecting information from clients and replying to frequently asked questions. This reduces human errors and provides a better customer experience, benefiting companies. According to Juniper Research, the global operational cost savings from using chatbots in banking are expected to reach $7.3 billion by next year.
Here are some examples of AI-powered chatbots implemented by prominent banks.
Amy, the chatbot used by British multinational investment bank HSBC, processes critical customer feedback and answers additional questions.
Amelia, the chatbot used by a Swedish financial group focuses on the employee experience and assists them with internal IT support.
Erica, the virtual assistant used by Bank of America, consults with users and helps them save money.
Human-robot collaboration benefits both sides. While robots automate and streamline back-end office processes, humans can focus on more strategic and creative operations. The following are some processes robots can handle.
Onboarding new customers
Performing security checks
Conducting trade finance operations and loan application processes
Inbounding calls related to routine queries such as account statements and transactions
You may have seen a movie called “The Wolf of Wall Street.” Do you remember the crowded atmosphere on the market floor? Well, now that algorithmic trading can use large datasets to analyze and identify trends and make trading decisions, there aren’t as many people conducting transactions like that.
Developers can train algorithms to scan users’ historical data (which includes not only bank documentation but also their digital footprint) and determine their credit score. Moreover, these algorithms can also give users recommendations on how to improve their credit.
Let’s take a look at how four companies in the fintech industry use AI in their businesses.
Enova focuses on serving non-prime consumers and small businesses, groups that are frequently underserved by traditional banks. They utilize AI and ML for advanced analytics and technology which facilitates responsible lending.
Scienaptic Systems is currently fundamentally disrupting the way consumer credit is administered. AI is involved in transforming data, learning from each interaction, and running predictive models. The results? $151 million in loss savings in just three weeks.
Underwrite.ai analyzes datasets from credit bureau sources and assesses credit risk for consumer and small business loan applicants. The company claims its services can reduce loan defaulting by 25-50%.
Kensho Technologies uses AI-powered databases, cloud computing, and natural language processing (NLP) to provide answers to complex financial questions. For example, they used this technology to predict the English pound’s drop in value days after Brexit.
According to the Cambridge Centre for Alternative Finance and the World Economic Forum, a majority of financial services companies say they've implemented AI technology in business domains like risk management (56%) and revenue generation through new products and processes (52%).
How does AI implementation benefit both businesses and customers?
Optimized workflow: AI reduces the possibility of human error by automating routine tasks for the customer support department like collecting information and answering frequently asked questions.
Higher user engagement: When users get an answer immediately, they are more satisfied. According to the Usability Engineering book, 10 seconds is the limit for keeping a user's attention focused. AI makes it possible for clients to get instant answers to their questions.
Secure payments: AI monitors users’ verification and overall payment security. In our blog posts about developing a money transfer website and building P2P apps, we’ve described the whole process of it.
Data-driven decision-making: AI offers insights often invisible to human eyes. Conclusions made from analyzing large amounts of data help to form reports and make predictions which in turn help to build actionable business strategies.
While AI is continuing to revolutionize the financial industry, there are three major risk factors which must be understood.
1. Embedded bias: This bias is defined as the ability of algorithms to prejudice and discriminate against certain groups of people. Why? The reason may be related to the way the datasets are organized. When utilized in the financial industry, the ability to explain AI results becomes critical, and inappropriate conclusions may expose businesses and individuals to vulnerabilities.
To avoid this, pay attention to (1) the data sets being provided, (2) whether you have access to logs of the AI decision-making process, and (3) whether the decision-making models can be tested.
2. Integration and implementation: Implementing an AI solution requires a deep understanding of the existing IT infrastructure within the company. Before integrating, you must consider what internal systems are in place to monitor the AI.
3. Transparency: The financial industry is often subject to regulations (which may vary greatly by region) that banks, credit unions, insurance companies, and other financial service companies must follow. These regulations also dictate how personal data is collected, stored, and processed.
The most popular use cases of AI in fintech are regulatory compliance, trends predictions, algorithmic trading, client verification, and communication. AI will become increasingly widespread in these areas in the coming years. Juniper Research predicts that successful banking-related chatbot interactions will achieve stratospheric growth by the year 2023, resulting in 826 million hours of labor saved, with 79% of successful chatbot interactions carried out through mobile banking apps.
AI has already revolutionized the fintech industry and its products. Implementing AI is not easy and requires careful management of both ethical and technical responsibilities. However, it’s no longer a luxury but instead a must-have for financial products and projects dealing with credit scores, customer communication, and financial trends. The influence of AI will increase in the next year, and use cases will certainly continue to evolve.
🦾 Is AI used in fintech?
🦾 What are the use cases of AI in fintech?
🦾 What are the benefits of AI implementation?
🦾 Are there any risks?
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