As the revolutionary shifts in the technology sector have arisen in recent years, the transformation of the financial industry is also taking a dizzying turn since the established banks cannot enable to avoid the necessity of adaptation to technological developments. Besides, there have also been for a while a lot of startups and small companies to provide alternative financial services for people with the help of game-changing technologies. With the rise of competition between the actors in today’s financial ecosystem, financial institutions must find innovative ways to satisfy the demands and expectations of customers that have changed in time. That is why we have experienced the rise of technological interventions within the financial sector through tools such as mobile wallets, artificial intelligence, machine learning, e.g., in recent years.
Today we are going to specifically talk about the impact of artificial intelligence in the fintech sector. Artificial intelligence (AI) can be identified as the development of computer systems capable of performing tasks that always require human intelligence, such as visual perception, speech recognition, decision-making, and cross-language translation.
As stated by Paul Sin, Deloitte AP Blockchain Lab Leader;
Contrary to traditional data analytics and robotic process automation, artificial intelligence has four distinctive features. The first one is that it will detect the change in data patterns and tune the model by itself. There is no longer the need for a data scientist to keep tuning the model. Secondly, even if the data quality is not perfect, with the help of artificial intelligence mechanisms, businesses can create useful insights about the clients, while making decisions. Thirdly, when the data is coming in, we do not need to store and process and transform it before we can create analytics. It can just stream through the neural network, and the AI engine can pick up the pattern and help businesses to serve the customers. Fourth, artificial intelligence can handle unstructured data. Therefore, businesses can apply AI in places like image recognition, or voice recognition e.g.
Until the past decade, artificial intelligence has seemed like a distant dream to humanity. Today we can see very clearly the usage of artificial intelligence in the many web-based services we use almost everywhere, on our mobile phones, in our cars, in our homes throughout the day. Google translation, Apple Siri are perhaps the most famous of these examples, but the cases we have come across are much more than that. Shortly, artificial intelligence (AI) is a game-changing technology that changes the way we live, move, and interact with each other. At that point, the financial sector is no exception in that game.
In financial services, artificial intelligence is mostly used in three significant areas. The first of which is machine learning to create a propensity model. In that context, it should be kept in mind that as a subset of umbrella term artificial intelligence, machine learning specifically refers to the algorithms that can learn and adapt over time. To get back to the issue at hand, banks and insurance companies take advantage of machine learning in their websites or mobile channels by creating real-time marketing. They predict the product propensity of their customers based on their behavioral data in real-time.
By accumulating a lot of data, these banks and companies can tap into this pool of resources and create useful business insights about their clients. It is possible to identify entities and patterns from this data, find clusters and correlations between the different types of entities and hence find relevant contextualized insights for every customer, which in turn can help companies and businesses to automate the categorization of clients based on their risk profile. Building on the categorization work, advisors can decide to offer financial products for the clients by associating them with each risk profile in an automated way.
The second application of artificial intelligence is natural language processing. As a branch of artificial intelligence, NLP can process data in the format of human language, and enable computer programs to read unstructured texts and detect non-compliance in the document. Therefore, companies can gain an understanding of customer perception and sentiment around their products, services, and brands. Hence, the business and companies can enable to recommend more available products and services for clients based on the transactions that occurred between the algorithm and the human user.
The third application is image recognition as one of the uses of computer vision that refers to the process of identifying and then, detecting an object in a digital image. Fintech startups always ask their customers to take a selfie and also take a picture of their identity documents. After then, both of them are matched for the aims of “Know Your Customer” (KYC) and onboarding through AI and Big Data technologies. Financial institutions monitor and conduct many data such as the demographic data of clients such as their name, e-mail, and home addresses, and the financial data obtained from customers’ investment, transaction activities, mortgages and credit cards, e.g.
While processing the customers’ data, one of the most crucial issues is to provide data protection. Because of the increasing extent of data breaches to the leading financial institutions in recent years, these businesses have tried to develop hyper-personalized strategies for data protection by preventing fraudulent activity.
So, how is it possible?
Data analysis tools supported by artificial intelligence collect and analyze data of users’ behavioral patterns to identify fraud attempts and incidents to prevent possible fraud scenarios. AI-based fraud prevention is realized with the help of machine learning that enables companies to examine events, factors, and trends from the past. Historical machine learning models find anomalies and fraud patterns, which in turn can allow machine learning to be adapted to finding relative risk of customers’ behaviors.
On the other hand, another advantage of AI can be observed in the credit scoring arena. ML algorithms can enable to convert customer data into a credit score by providing more nuanced evolution of data that would not seem relevant to measure the “risk” of potential borrowers. Today, banks, creditors, and fintech startups can “fairly” measure the creditworthiness of customers, using alternative data from social media posts to internet activities through the Artificial Intelligence (AI) mechanisms. Traditional credit scoring systems had to make assumptions based on historical data to predict future credit-worthiness. Otherwise, a self-learning AI can analyze the data that is obtained from a wide range of sources, and provide detail predictions that are not so likely for a standard credit scoring model to make.
Therefore, we can say that the collaboration between two trends, fintech, and artificial intelligence, has dramatically enhanced the services of financial institutions, and thereby customer experience. Customers can navigate the platforms more quickly, can get quick support and personalized help from the banks, companies, and fintech startups in the new digital era.