Almost every industry, including banking and finance, has been significantly disrupted by artificial intelligence. The industry is now more customer-centric and technologically relevant thanks to the inclusion of AI for banks apps and services.
By increasing productivity and making decisions based on incomprehensible data to a human agent, AI-based systems can help banks cut costs. Additionally, intelligent algorithms can quickly detect false information.
According to a Business Insider report, almost 80% of banks know the potential advantages AI could bring to their industry. According to another report, banks are expected to save $447 billion by using AI for banks apps by 2023. These figures show that the banking and finance industry is moving quickly toward AI to increase productivity, decrease costs, and improve efficiency.
In this article, we’ll learn about the main uses of AI in the banking and finance industry and how its exceptional advantages are redefining customer service.
AI for Banks and Finance Applications
The world we live in now includes artificial intelligence, and banks have already begun incorporating this technology into their goods and services.
Here are some significant AI for banks applications in the banking sector that will allow you to take advantage of the technology’s many advantages. So let’s get started.
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Security online and spotting fraud
Large numbers of digital transactions happen every day as users use apps or online accounts to pay bills, withdraw money, deposit checks, and do much more. As a result, the banking industry must increase its efforts in cybersecurity and fraud detection.
This is when banking artificial intelligence enters the picture. AI for banks assist banks in reducing risks, tracking system flaws, and enhancing the security of online financial transactions. AI and machine learning can quickly spot fraudulent activity and notify both customers and banks.
For instance, Danske Bank, the largest bank in Denmark, replaced its previous rules-based fraud detection system with a fraud detection algorithm. The bank’s ability to detect fraud was increased by 50% thanks to this deep learning tool, which also reduced false positives by 60%. While routing some cases to human analysts for additional examination, the system also automated a large number of crucial decisions.
AI can assist banks in managing online threats. The financial sector was the industry most frequently targeted in 2019, accounting for 29% of all cyberattacks. Banks can respond to potential cyberattacks before they impact personnel, clients, or internal systems thanks to the continuous monitoring capabilities of artificial intelligence in the financial services industry.
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Chatbots
Without a doubt, chatbots are one of the best examples of how artificial intelligence is used in banking. They can work around the clock once deployed, in contrast to humans who have set working hours.
They also continue to learn more about a specific customer’s usage habits. It aids in their effective understanding of user requirements.
The banks can guarantee that they are accessible to their customers 24 hours a day by integrating chatbots into their banking apps. Additionally, chatbots can provide personalized customer support and make appropriate financial service and product recommendations by comprehending customer behavior.
Erica, a virtual assistant created by Bank of America, is among the best examples of AI chatbots in banking applications. This AI chatbot can take care of tasks like updating card security and reducing credit card debt. In 2019, Erica handled more than 50 million client requests.
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Credit and loan decisions
In order to make better, safer, and more profitable loan and credit decisions, banks have begun implementing AI-based systems. Currently, many banks still only consider a person’s or business’s creditworthiness based on their credit history, credit scores, and customer references.
One cannot ignore the fact that these credit reporting systems frequently contain errors, omit real-world transaction history, and incorrectly classify creditors.
Customers with little credit history can use an AI-based loan and credit system to analyze their patterns of behavior to assess their creditworthiness. Additionally, the system notifies banks of specific actions that might raise the risk of default. In short, these technologies are significantly altering the way that consumer lending will be done in the future.
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Follow market trends
Banks can process huge amounts of data and forecast the most recent market trends, currencies, and stocks thanks to artificial intelligence in financial services. Modern machine learning methods offer investment suggestions and assist in evaluating market sentiment.
AI for banks also recommends when to buy stocks and issues alerts when there is a potential risk. This cutting-edge technology also helps to speed up decision-making and makes trading convenient for both banks and their clients due to its high data processing capacity.
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Gathering and analyzing data
Every day, financial and banking institutions record millions of transactions. Due to the enormous amount of information generated, it becomes difficult for employees to collect and register it. It becomes impossible to structure and record such a large amount of data without making any mistakes.
AI-based creative solutions can aid in effective data collection and analysis in such circumstances. Thus, the overall user experience is enhanced. Additionally, the data may be used to identify fraud or make credit decisions.
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Customer encounter
Customers are always looking for a more convenient experience. For instance, ATMs were successful because they allowed customers to access necessary services like money withdrawal and deposit even when banks were closed.
More innovation has only been spurred by this level of convenience. Customers can now use their smartphones to open bank accounts from the comfort of their homes.
Artificial intelligence integration will improve user convenience and the customer experience in banking and finance services. AI technology speeds up the recording of Know Your Customer (KYC) data and eliminates mistakes. Additionally, timely releases of new goods and financial offers are possible.
Clients can avoid the hassle of going through the entire process manually by using AI to automate eligibility for cases like applying for a personal loan or credit. Furthermore, AI-based software can speed up approval processes for services like loan disbursement.
Additionally, AI banking aids in the precise collection of client data for error-free account setup, ensuring a positive customer experience.
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Management of risk
The banking and financial sectors are significantly impacted by external global factors like exchange rate fluctuations, natural disasters, and political unrest. Making business decisions with extra caution is essential in such uncertain times. AI-driven analytics can provide a reasonably accurate forecast of future events, assisting you in remaining organized and making timely decisions.
AI also assists in identifying risky applications by calculating the likelihood that a client will default on a loan. By examining historical behavioral patterns and smartphone data, it forecasts this future behavior.
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Regulation observance
One of the most strictly regulated industries in the world is banking. The use of banks by banking customers in committing financial crimes is prohibited, and governments use their regulatory authority to ensure that banks have acceptable risk profiles and don’t experience widespread defaults.
Banks typically maintain an internal compliance team to address these issues, but manual solutions take a lot longer and cost a lot more money. Banks must constantly update their procedures and workflows to comply with the compliance regulations, which are also frequently changed.
Deep learning and Natural Language Processing (NLP) are used by AI to read new compliance requirements for financial institutions and enhance their decision-making. Even though AI banking can’t completely replace a compliance analyst, it can speed up and streamline their processes.
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Predictive modeling
Predictive analytics and general-purpose semantic and natural language applications are two of the most prevalent use cases for AI. Data can have specific patterns and correlations that AI for banks can identify that were previously invisible to traditional technology.
These patterns might point to underutilized cross-sell or sales opportunities, operational data metrics, or even revenue-impacting metrics.
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Automation of processes
By automating time-consuming repetitive tasks, robotic process automation (RPA) algorithms improve operational efficiency and accuracy while lowering costs. Users can now concentrate on harder tasks requiring human interaction.
RPA is currently being successfully used by banking institutions to speed up transactions and improve efficiency. For instance, CoiN technology from JPMorgan Chase reviews documents and extracts data from them much more quickly than humans can.
Why is it necessary for the banking industry to adopt AI first?
Banks are vying to lead the AI revolution and for good reason. The banking sector has been working for many years to change from a people-centric to a customer-centric organization. This change has compelled banks to adopt a more all-encompassing strategy in order to satisfy the needs and expectations of their clients.
Banks must start considering how to better serve their customers as their primary focus has shifted to them. Customers now demand scale from their banks and expect them to be there for them whenever they need it, which entails being accessible around-the-clock, every day of the week. Banks can accomplish this using AI.
Banks must first overcome some of their own internal obstacles, including outdated systems, data silos, asset quality, and tight budgets, in order to meet these customers’ expectations. These are just a few of the problems that prevent banks from changing quickly enough to keep up with customer demands, so it makes sense that many banks are looking to artificial intelligence (AI) to enable this change. The question is, how?
How can a bank become an AI pioneer?
After seeing how AI is used in banking, this section will examine the steps banks can take to adopt AI widely and modernize their processes while paying careful attention to the four critical factors: people, governance, process, and technology.
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Create an AI strategy
Creating an enterprise-level AI strategy while keeping the organization’s objectives and core values in mind is the first step in the AI implementation process.
In order to identify areas where people and processes are lacking, internal market research is essential. Ensure that your AI strategy complies with all applicable rules and regulations. Banks can assess the current global industry norms as well.
In order to provide clear directions and guidance for AI adoption across the bank’s various functional units, the internal practices and policies related to talent, data, infrastructure, and algorithms must be refined as the last step in the formulation of an AI strategy.
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Plan a use case-driven process
Finding the highest-value AI opportunities while coordinating with the bank’s processes and strategies is the next step.
Banks must assess how much they must integrate AI for banks solutions into their existing or modified operational procedures.
The technology teams should conduct checks on testing viability after identifying potential AI and machine learning use cases in banking. They need to investigate every angle and spot any implementation gaps. They must choose the cases that have the best chance of success based on their evaluation.
The mapping of AI talent is the last step in the planning process. Several specialists, algorithm programmers, or data scientists are needed by banks to create and implement AI for banks solutions. If they don’t have internal experts,
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Create and use
Executing the process is the next step for banks after planning. They must first create prototypes to comprehend the limitations of the technology before creating full-fledged AI systems. Banks must gather pertinent data and feed it to the algorithm in order to test the prototypes. The data must be accurate because the AI model learns and develops using it.
Banks must test the AI model to interpret the results after it has been trained and made ready. The development team will benefit from a trial like this one to better understand how the model will function in actual use.
The trained model must be deployed as the final step. Production data starts to arrive as soon as it is deployed. As more and more data begin to arrive,
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Run and Keeping an eye on
AI banking solutions implementation necessitates ongoing calibration and monitoring. The operation of the AI model needs to be thoroughly monitored and evaluated, so banks must develop a review cycle. Banks will benefit from this as they manage cybersecurity threats and carry out their operations effectively.
The operation stage of the AI for banks model will be impacted by the ongoing flow of new data. Therefore, banks should implement the necessary procedures to guarantee the accuracy and fairness of the input data.
Examples of artificial intelligence in banking in the real world
A select few large banks have already begun to use artificial intelligence technologies to raise customer satisfaction, reduce fraud and cybersecurity risks, and improve service quality.
Here are a few instances of banking organizations using AI to its full potential in the real world.
- JPMorgan Chase: To identify malware, trojans, and phishing campaigns, researchers at JPMorgan Chase have created an early warning system using AI for banks and deep learning methods. According to researchers, it takes a trojan about 101 days to infiltrate corporate networks. The early warning system would give plenty of advance notice before the attack actually occurs.
As hackers prepare to send harmful emails to employees in order to infect the bank, they can also send alerts to the cybersecurity team of the bank.
- The best application of AI for banks in personal banking is Capital One’s Eno, an intelligent virtual assistant. Eno is not the only company using virtual card numbers to stop credit card fraud; Capital One is as well. They are also developing computational creativity, which teaches computers to be imaginative and comprehensible.
- Several investment banks, including Goldman Sachs and Merrill Lynch, have added analytical AI-based tools to their standard operations in addition to commercial banks. Additionally, a lot of banks have started using Alphasense, an AI-based search engine that analyzes keyword searches and market trends using natural language processing.
