Artificial Intelligence (AI) is changing the business landscape, and the banking sector is no exception. From mobile banking to customer service and loan applications, artificial intelligence has transformed nearly every facet of the finance and banking industry.
According to the Cambridge Centre for Alternative Finance (CCAF), 56% of institutions
have already implemented the technology in risk management.
Others are in the process of implementing AI in their business processes. Market data shows that AI adoption in the banking industry is projected to reach $64.03 billion by 2030.
AI has impacted banking from all angles—office, front, middle, and back. Even if you don’t know any of the complex processes that are powered by AI, chances are you have interacted with a banking customer service chatbot which runs on AI.
But exactly how are these institutions incorporating AI? More importantly, what are the benefits of artificial intelligence in banking and finance? This guide will answer all these questions and explore the challenges experienced in adopting AI in the financial sector.
7 Ways AI Is Used in Banking and Finance
Artificial intelligence has become an integral part of our world, and financial institutions have incorporated it into many of their products and services.
Here are some of the major AI applications in the industry.
Banks are some of the most highly regulated institutions, and compliance with regulatory authorities is critical. Failure to comply with the set regulations can lead to penalties and, worst-case scenario, revocation of the license.
Governments use regulatory authority to ensure banks aren’t using customers’ data and monetary resources to commit financial fraud and that these institutions have an acceptable risk profile to avoid large-scale defaults.
So, where does artificial intelligence come in?
As with any other industry, banking compliance requirements are subject to change, and these institutions need to update their workflows and processes to align with the changes in the regulatory framework.
Artificial intelligence in banking uses deep learning technology and NLP to read new compliance requirements for banks and improve their decision-making process. Even though AI can’t replace compliance analysts, it makes their operations faster and more efficient.
Customer service is one of those departments that rely heavily on AI. If you’re a frequent internet user, chances are you’ve interacted with a chatbot.
Chatbots deliver high ROI in cost savings. They also improve communications with business customers and are available 24/7. These bots run on AI and can handle most of the common tasks in banking, such as balance inquiries, fund transfers, etc. This helps reduce traffic from other channels, such as contact centers and Internet banking.
3. Data Collection and Analysis
Another area that AI plays a critical role in banking is in data collection and analysis.
AI algorithms process massive volumes of data and extract valuable insights from data. These insights can help the management make data-driven decisions.
For instance, the analysis can help financial institutions easily predict demand and market trends. Furthermore, customer data analysis through AI-powered mobile banking apps also helps these institutions deliver personalized service and improve the user experience.
4. Risk Management
Global factors such as currency fluctuations, political instability, and natural disasters often impact the banking industry.
During periods of such unrest, it’s important to make informed business decisions. Generative AI in banking provides analytics that gives a clear picture of what is to come, helping them stay prepared and make informed decisions.
Artificial intelligence also helps banks stay risk-averse by evaluating the probability of a client failing to pay based on parameters such as income, credit score, and past behavioral patterns.
Cybersecurity threats are a global problem, impacting all businesses irrespective of size or industry. A 2018 Accenture study
evaluated 30 banking applications and found that all had vulnerabilities, ranging from insecure authentication to code tampering and insecure storage.
From ineffective cryptography to lack of secure data storage, there are many reasons why online banking portals and apps pose a special threat.
Artificial intelligence can significantly improve the effectiveness of cybersecurity systems by using data from previous threats and identifying potential vulnerabilities. In addition to curbing external threats, AI can also monitor internal threats and suggest corrective actions.
6. Loan Applications
The most potential application of artificial intelligence and machine learning in banking is arguably in loan applications.
The AI algorithm assesses a customer’s ability to pay and their likelihood to make plans to pay off debt. AI-powered credit scoring solutions are in high demand because there are billions of unbanked people worldwide, and only a proportion of them qualify for credit.
AI-based loan systems can analyze the behavior and patterns of customers with poor or limited credit history to determine their creditworthiness.
7. Customer Service
AI banking apps are designed to improve customer service and the overall customer experience. Implementing AI and machine learning in banking helps these institutions track user behavior and deliver highly personalized customer service.
Intelligent machine-learning apps can monitor user behavior and derive valuable insights based on history, trends, and search patterns. These insights can help service providers deliver personalized recommendations to users.
Additionally, AI-powered chatbots can provide customers with personalized customer service 24/7, irrespective of time zones. They can answer pressing customer queries, direct customers to useful resources, and help them navigate the site.
Top Benefits of Artificial Intelligence (AI) in Banking and Finance
Adopting AI in banking and finance offers many benefits to institutions and end users. Some of the key benefits of AI adoption in banking include.
1. Better Customer Experience
Customer experience is a powerful force that can make or break a business. Studies show that 86% of consumers
will leave a brand they trusted after one or two poor customer experiences.
That said, it comes as no surprise that 81% of organizations
, banking included, cite customer experience as a competitive differentiator.
Poor customer experience in the banking sector can take many forms, including long wait times, poor support, slow website, rude customer service reps, etc. Artificial intelligence can help improve customer experience by solving some of these issues.
For example, chatbots can help address customer support issues in the banking sector. Unlike employees, chatbots are available 24/7 and can answer customer queries in a way that resembles human interaction.
In addition to fielding customer service queries, banks are also using chatbots to upsell products. These bots provide merchants with info about loan offerings they weren’t aware of that can help resolve payment or credit issues.
2. Improved Loan and Credit Decisions
Poor loan and credit decisions are one of the top reasons financial institutions, like banks, go bankrupt. Thanks to artificial intelligence, banks can now assess customers’ creditworthiness and assign a score that helps loan officers make informed decisions.
AI-based systems and machine learning algorithms can study customer behaviors and patterns to determine if the customer will be able to pay the debts on time. These systems consider many parameters, such as credit history, transaction analysis, income, work experience, and previous loan payment history for credit scoring.
3. Enhanced Security
In an age where cybersecurity threats are growing by the day, tightening security in the banking sector is critical. Artificial intelligence systems help improve banking security by monitoring transactions in real time, detecting fraud, and flagging suspicious transactions.
This improves the overall security of the banking and financial services industry, reducing the risk of fraudulent activities.
4. Increased Efficiency
AI can automate repetitive and time-consuming tasks like loan underwriting, compliance monitoring, and fraud detection. This increases efficiency and frees employees’ time to focus more on other revenue-generating tasks.
Challenges Facing AI Adoption in the Banking and Financial Services Sector
While AI in the banking and financial services sector has shown promising results, its adoption is not without challenges. Here are some of the bottlenecks to AI adoption in the banking sector.
Financial institutions collect massive volumes of data daily, and these organizations need to implement adequate measures to avoid any breaches or violations of data protection laws.
As such, finding a technology partner familiar with AI and banking is crucial. This is not always easy as the right partner needs to not only be adept with banking AI technology but also provide various security options to ensure customer data is protected.
Artificial intelligence bias is one of the biggest risks in using AI in the banking sector and across industries. This is because AI models are developed by people who bring their assumptions and biases to the training of the ML model.
These biases can be conspicuous when the model is deployed, sometimes resulting in unfavorable or troubling results.
Lack of Quality Data
Financial institutions need quality and structured data for training and validation before deploying an AI-based solution.
Quality data is required to ensure the system simulates real-life situations. Problems can arise in data collection and processing. For instance, if the data is not machine-readable, it may lead to anomalies in the AI model behavior.
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