Artificial Intelligence has more uses in industries that are rich in data and are tasked with number crunching and data entry duties. The banking and financial services sector, therefore, is the perfect match for AI and its applications. It presents a number of functions for different industries including the banking sector.
The Fintech or the Financial Technology sector has been making use of the AI technology for quite a few years now and have been able to come up with some of the most remarkable solutions for the banking sector. The latter, in turn, has been slow or even reluctant at times to adopt modern technology into its existing frameworks.
The primary reason behind this is the lack of ability or intellectual resources to implement such technology. Another is the fact that most the financial sector, in general, has to consider the aspect of security for their clients. Digital systems always run the risk of being susceptible to online fraud and cyber attacks. This makes banks wary of using modern technology. Despite the reality that financial firms that use advanced tech systems have reported significantly positive growth in revenues, the uptake of AI-enabled systems in banking has been slow.
The Role of Big Data in Transforming Banking
Big data has been a buzz word of sorts for the tech industry for quite a few years now. In 2013, nearly 64% of companies were using big data to their advantage.
The increasing progression in technology has pushed this number even further in the following years. It has transformed the way of doing business for many companies, allowing them to interpret data like never before.
The banking and financial sector too generates large amounts of data that can be used to provide tailored services to clients. Therefore, big data has numerous applications for financial services firms from financial management to anti-money laundering or AML compliance solutions.
According to industry experts, big data will continue to be the driving force for the technological evolution in different companies. Predictive analytics have allowed companies to implement models that can predict customer behavior and provide insights regarding market segmentation, targeting, and even fraud detection.
Banks using big data tools to provide services have seemed to gain a competitive advantage by allowing for smoother yet more customized services.
Enhancing Quality of Value Offerings for Customers
With the help of big data analytics, the financial services sector has been able to improve their service offerings and provide more personalized products to clients. They use customer spending pattern, transaction histories and earning and savings to provide banking services that are better suited to their needs. They also use such data to provide financial management services to customers through virtual assistants. They can provide advice to customers about increasing their savings and assist them with their investment options.
Other services enable insurance providers to streamline their methods by introducing efficient methods for filing claims. Instead of going through lengthy procedures, an automated process can be initiated through a chatbot that can use a series of standardized questions to guide a client for filing a claim.
Attracting Better Investment Opportunities
Using big data has enabled banks and financial institutions to gain a competitive advantage in their service offerings. It has therefore allowed them to attract better investments and increase their funding. This enables them to add to their working capital and make sure they have enough cash to advance their operations.
Predictive models developed using big data enable banks to detect patterns in their clients’ transactions. It uses machine learning algorithms to analyse client transactions and establish a normal pattern for them. Using them as a benchmark, banks and financial institutions are then able to detect suspicious transactions that may entail fraud.
Meeting Compliance Requirements
The regulatory environment has become more stringent for the financial services industry, increasing their compliance obligations.
In the wake of scandals like the Panama Papers leak, financial regulators have increased compliance obligations for banks all over the world.
In order to keep up with the constantly changing regulatory requirements, financial firms are now adopting methods of AML compliance that leverage big data. They use predictive analytic techniques and AML screenings to identify high-risk clients and thus treat them with extra care.
Modern AI Solutions
The rapidly progressing AI technology has fine-tuned the systems and solutions that are now available for banks and other business markets in general.
Add to this the fact that AI applications being developed currently are remarkably easy to integrate for businesses through APIs (Application Programming Interfaces).
Interactive chatbots and digital assistants have enhanced customer service functions for companies, allowing them to reduce their costs and make their services more efficient.
Businesses can now assist customers in real-time, 24/7, without the need to hire an army of customer sales representatives.
Other AI systems use predictive analysis techniques to assess customer needs and target them more accurately. They can also enable enterprises to provide customized services and to model their prices according to each client’s needs.
AI tools also have the ability to detect fraud effectively through transaction monitoring and online identity proofing systems, thus providing better security for businesses. All such applications of AI can prove highly effective for the banking sector.
The Need for AI in Banking
One of the major convenience lent by modern technology to people today is increased connectivity and high accessibility. The proliferation of smartphones and other mobile devices has eliminated the need for physical presence to gain most services for people.
Millennials, in particular, are highly dependent on modern tech tools and require quick and efficient services. They expect the same from the financial services they require. Internet and mobile banking is a prerequisite amongst their requirement.
A majority of the banks and financial services firms have managed to bring their services online, allowing easy access to clients. The question remains whether these services are as fast and efficient as expected by people nowadays?
The existing legacy systems that prevail in most organisations are, however, slow and unproductive. They run on predefined rules and instructions that are incapable of identifying patterns that are outside their specified set of instructions. AI, on the other hand, has the ability to use customer data and detect patterns.
How AI can Transform the Banking Sector
Just like any other business, AI has the potential to transform the financial services sector by overhauling its functions from the ground up. It is enabling banks and other financial institutes to improve their customer services, compliance functions and their cybersecurity structures. Some of the ways AI is helping the banking sector include;
Artificial intelligence is often deemed notorious for automating repetitive functions in companies and is blamed for eliminating jobs. This, however, is only partially true.
AI is entirely capable of automating simple repetitive tasks, but only to the extent of assisting human workers rather than eliminating the need for them.
One example of this is AI-enabled chatbots that use RPA or Robotic Process Automation to address client queries and complaints. They have the ability to respond to customer queries instantly and round the clock.
Routine questions and queries that come in through customer service channels can, therefore, be addressed to instantly within seconds through chatbots.
If the problem gets more complicated or elaborate the chatbot directs the customer to a human representative, to guide them further. This has significantly reduced the workload of customer service representatives, thereby increasing their efficiency.
Providing Personalised Services
One of the most useful perks of AI tools is that they can analyze large amounts of customer data – structured or unstructured – within seconds.
Using that data it can detect patterns in clients’ transactions, spending behavior, income and savings to provide useful insights to banking managers and executives.
It further enables them to offer tailored services to clients and pitch banking products to customers accordingly. Banks are now using AI-powered digital assistants to provide wealth management services to customers. The assistant asks a customer a specified set of questions, and depending upon their answers it can offer a product or service to them.
Enhanced Risk Management
Banks have to go through an elaborate and complicated process to sanction credit to customers. The procedure requires extreme precision and accuracy on part of the bank, which makes it more drawn out and slow.
AI, however, has been monumental in solving this problem. With the ability to process large amounts of data, AI tools can develop risk profiles for a client within minutes.
The processes that are normally drawn out for weeks and even months in certain cases can be wrapped up within a day. Risk management tools use the provided customer information, transactions, market trends, and the client’s financial activity to gauge the risk involved in providing credit to a client or business.
These systems can perform the process with more precision and accuracy than human officers. They can also eliminate redundancies in the process, thus allowing the banks to focus on more important tasks.
One of the best applications that AI has for the banking sector is perhaps real-time fraud detection.
AI systems are now available for banks that can detect fraudulent activity in a customer’s transactions through behavioral analytics. The suspicious activity is then reported to the bank, which can take measures to block it effectively.
Other AI applications are able to perform identity proofing of customers remotely. They authenticate customers through document verification and biometric facial recognition. This enables banks to weed out fraudsters and scammers before they can do much harm.