Artificial intelligence (AI) technology has become a critical breakthrough in almost every industry, and banking is no exception. The introduction of AI into banking applications and services has made the sector more customer-centric and technologically relevant.
AI-based systems can help banks cut costs by improving productivity and making decisions based on information that is incomprehensible to humans. In addition, intelligent algorithms are able to detect anomalies and fraudulent information in seconds.
A Business Insider report states that nearly 80% of banks are aware of the potential benefits that AI represents for their sector. Another report states that banks are projected to save $447 billion by 2023 using AI applications. These figures show that the banking and financial sector is rapidly moving towards AI to improve efficiency, service, performance and ROI, as well as reduce costs.
In this article, we will learn about the main applications of AI in the financial and banking sector and how this technology is changing the customer experience due to its exceptional benefits.
Application of AI in banking and finance
AI technologies have become an integral part of the world we live in, and banks have begun integrating these technologies at scale into their products and services to stay relevant.Here are some top AI applications in banking where you can take advantage of the many benefits of this technology. So let’s dive in!
Cyber Security and Fraud Detection
A huge number of digital transactions take place every day, with users paying bills, withdrawing money, depositing checks and more through apps or online accounts. Thus, the banking sector is increasingly in need of increased cybersecurity and fraud detection efforts.
This is where artificial intelligence in banking comes into play . AI can help banks improve the security of online finance, track down loopholes in their systems, and mitigate risk. AI along with machine learning can easily detect fraudulent activities and alert customers as well as banks.
For example, Danske Bank, Denmark’s largest bank, has implemented a fraud detection algorithm to replace their old rule-based fraud detection system. This deep learning tool increased the bank’s fraud detection capabilities by 50% and reduced false positives by 60%. The system also automated many important decisions, routing some cases to human analysts for further study.
AI can also help banks deal with cyber threats. In 2019, the financial sector accounted for 29% of all cyber attacks , making it the most targeted industry. With AI continuous monitoring capabilities in financial services, banks can respond to potential cyberattacks before they affect employees, customers, or internal systems.
Undoubtedly, chatbots are one of the best examples of the practical application of artificial intelligence in the banking industry . Once deployed, they can work 24/7, unlike people who have fixed hours.
In addition, they continue to learn about the usage patterns of a particular customer. This helps them understand the user’s requirements effectively.
By integrating chatbots into banking applications , banks can ensure they are available 24/7 to their customers. Moreover, by understanding customer behavior, chatbots can offer personalized customer support and recommend suitable financial services and products accordingly.
One of the best examples of an AI chatbot in banking applications is Erika, a virtual assistant from Bank of America. This AI chatbot can perform tasks such as credit card debt reduction and card security updates. Erica handled over 50 million client requests in 2019.
Read also : How much does it cost to develop a chatbot?
Credit and credit solutions
Banks have begun implementing AI-based systems to make more informed, safer and more profitable lending and lending decisions. At present, many banks are still too limited to use credit history, credit scores, and customer references to determine the creditworthiness of an individual or company.
However, it cannot be denied that these credit reporting systems are often riddled with errors, lack of real transaction history, and misclassification of creditors.
An AI-based loan and credit system can learn the behaviors and patterns of credit-limited customers to determine their creditworthiness. In addition, the system sends warnings to banks about certain actions that may increase the likelihood of default.
Tracking market trends
Artificial intelligence in the financial services industry helps banks process large amounts of data and predict the latest market trends, currencies and stocks. Advanced machine learning techniques help gauge market sentiment and suggest investment options.
AI for banks also suggests the best time to invest in stocks and warns of potential risk. With its high processing power, this new technology also helps speed up decision making and makes trading convenient for both banks and their clients.
Data collection and analysis
Banking and financial institutions record millions of transactions every day. Since the amount of information generated is huge, collecting and recording it becomes an overwhelming task for employees. Structuring and writing such a huge amount of data without errors becomes impossible.
In such scenarios, innovative AI-based solutions can help with efficient data collection and analysis . This in turn improves the overall user experience. This information may also be used to detect fraud or make credit decisions.
Experience working with clients
Customers are constantly looking for the best experience and convenience. For example, ATMs were successful because customers could use basic deposit and withdrawal services even when banks were closed.
This level of convenience has only inspired new innovations. Customers can now open bank accounts from the comfort of their homes using their smartphones.
Integrating artificial intelligence into banking and financial services will further enhance the consumer experience and enhance user experience. Artificial intelligence technology reduces the time it takes to record Know Your Customer (KYC) information and troubleshoot errors. In addition, new products and financial proposals can be released on time.
Eligibility for cases such as applying for a personal loan or loan is automated using AI, meaning customers can save the hassle of doing the entire process manually. In addition, AI-based software can reduce the approval time for services such as issuing a loan.
AI banking also helps accurately collect customer information to set up accounts without errors, ensuring a seamless customer experience.
[Also read: 5 ways the fintech industry is using AI to attract millennials ]
Management of risks
External global factors such as currency fluctuations, natural disasters or political unrest have a major impact on the banking and financial industries. In such volatile times, it is essential to make business decisions with the utmost care. AI-driven analytics can give you a pretty good idea of what’s going to happen and help you stay prepared and make timely decisions.
AI also helps find risky applications by assessing the likelihood that a client will default on a loan. It predicts this future behavior by analyzing past behavior patterns and smartphone data.
Banking is one of the highly regulated sectors of the economy worldwide. Governments use their regulatory powers to ensure that bank customers do not use banks to commit financial crime and that banks have acceptable risk profiles to avoid large-scale defaults.
In most cases, banks have an internal compliance team to address these issues, but these processes take much longer and require a huge investment if done manually. Compliance rules are also subject to frequent change and banks need to keep their processes and workflows up to date with these rules.
AI uses deep learning and NLP to read new compliance requirements for financial institutions and improve their decision making. While AI banking cannot replace compliance analysts , it can make their operations faster and more efficient.
One of the most common use cases for AI includes general purpose semantic and natural language processing applications, as well as widely applied predictive analytics . AI can detect certain patterns and correlations in data that traditional technologies could not detect before.
These patterns can indicate untapped sales opportunities, cross-selling opportunities, or even operational data metrics, resulting in a direct impact on revenue.
Robotic process automation (RPA) algorithms increase operational efficiency and accuracy, and reduce costs by automating time-consuming, repetitive tasks. It also allows users to focus on more complex processes that require human intervention.
To date, banking institutions have been successfully using RPA to improve transaction speed and efficiency. For example, JPMorgan Chase’s CoiN technology scans documents and extracts data from them much faster than a human can.
How to become a bank with artificial intelligence?
Now that we have seen how AI is being used in the banking industry , in this section we will look at the steps that banks can take to implement AI on a large scale and evolve their processes while paying due attention to the four critical factors – people, management. , process and technology.
Step 1: Develop an AI strategy
The process of implementing AI begins with the development of an AI strategy at the enterprise level, taking into account the goals and values of the organization.
It is critical to conduct internal market research to find gaps in people and processes that AI technology can fill. Make sure your AI strategy is in line with industry standards and regulations. Banks can also evaluate current international industry standards.
The final step in formulating an AI strategy is to refine internal practices and policies related to people, data, infrastructure, and algorithms to provide clear guidance and guidance on how to implement AI across the various business units of the bank.
Step 2: Plan a case-based process
The next step involves identifying the most valuable AI opportunities in line with the bank’s processes and strategies.
Banks should also evaluate the extent to which they need to implement AI-based banking solutions as part of their current or changed operational processes.
After identifying potential use cases for AI and machine learning in banking , tech teams should conduct feasibility reviews. They should consider all aspects and identify gaps for implementation. Based on their assessment, they must select the most feasible cases.
The final step in the planning phase is identifying AI talents. Banks require a range of experts, algorithm programmers or data scientists to develop and implement AI solutions. If they lack in-house experts, they can outsource or work with a technology provider.
Step 3. Development and deployment
After planning, the next step for banks is execution. Before developing full-fledged artificial intelligence systems, they need to first create prototypes in order to understand the shortcomings of the technology. To test prototypes, banks need to collect relevant data and pass it to the algorithm. The AI model is trained and built on this data; so the data must be accurate.
Once the AI model is trained and ready, banks must test it in order to interpret the results. Such a test will help the development team understand how the model will work in the real world.
The last step is to deploy the trained model. After deployment, production data begins to flow. As more and more data comes in, banks can improve and update the model regularly.
Step 4: Work and control
The implementation of AI-based banking solutions requires constant monitoring and calibration. Banks need to develop a validation cycle to comprehensively monitor and evaluate the performance of the AI model. This, in turn, will help banks manage cybersecurity threats and securely execute transactions.
The continuous flow of new data will influence the AI model during the operational phase. Therefore, banks must take appropriate measures to ensure the quality and validity of the data entered.
Real examples of the use of artificial intelligence in the banking sector
Several large banks have already begun using AI technologies to improve customer experience, detect cybersecurity and fraud threats, and improve the customer experience.
Here are some real examples of banking institutions that are taking full advantage of AI.
JPMorgan Chase: Researchers at JPMorgan Chase have developed an early warning system using artificial intelligence and deep learning techniques to detect malware, trojans and phishing campaigns. Researchers say the Trojan takes about 101 days to compromise a company’s networks. An early warning system will provide sufficient warning before an actual attack occurs.
It can also send alerts to the bank’s cybersecurity team as hackers prepare to send malicious emails to employees to infect the network.
Capital One: Capital One’s Eno, an intelligent virtual assistant, is the best example of AI in personal banking. Apart from Eno, Capital One also uses virtual card numbers to prevent credit card fraud. In the meantime, they are working on computational creativity, which teaches computers to be creative and explainable.
In addition to commercial banks, a number of investment banks such as Goldman Sachs and Merrill Lynch have also integrated AI-based analytics tools into their day-to-day operations. Many banks have also started using Alphasense, an AI-powered search engine that uses natural language processing to detect market trends and analyze keyword searches.
Now that we’ve covered real -life examples of using AI in banking , let’s delve into the challenges that exist for banks using this new technology.
Challenges with wider adoption of AI in finance and banking
The widespread adoption of advanced technologies such as AI is not without challenges. From the lack of reliable and high-quality data to security issues, banks using AI technologies face a number of challenges.
So, without further ado, let’s go through them one by one:
- Data Security: One of the key concerns of AI in banking is that the amount of sensitive data collected requires additional security measures. Therefore, it is important to find the right technology partner that offers a variety of security options to ensure your customers’ data is handled properly.
- Lack of quality data: Banks need structured and quality data to train and validate before deploying a full AI banking solution. Good quality data is essential to ensure that the algorithm is applied to real situations. Also, if the data is not in a machine-readable format, it can lead to unexpected behavior of the AI model. As such, banks looking to adopt AI need to change their data policies to mitigate all privacy and compliance risks.
- Lack of explainability: AI-based systems are widely applicable in decision-making processes as they eliminate errors and save time. However, they may follow biases learned from previous cases of shortsightedness in human judgment. Minor inconsistencies in AI systems do not take long to escalate and create large-scale problems, thereby risking the reputation and functioning of the bank.
To avoid disasters, banks must offer an appropriate level of explainability for all decisions and recommendations presented by AI models. Banks must understand, validate and explain how the model makes decisions.
How Appinventiv can help you on your AI journey
As we can see, AI and banking go hand in hand due to the many benefits this technology offers. According to Forbes, 65% of CFOs expect a positive change from the use of AI and machine learning in banking. Thus, all banking institutions should invest in AI solutions to offer new experiences and superior services to customers.
At Appinventiv, we work with banks and financial institutions on a variety of custom AI and machine learning models that help increase revenue, reduce costs, and mitigate risk across departments.
If you are also looking for AI development services , talk to our experts. We can help you create and implement a long-term AI strategy in banking and meet your needs in the most technologically and cost-effective way.
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