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Artificial Intelligence: Game-Changer in Banking Industry

Apart from profit generation as their main goal, financial institutions also strive to achieve cost reduction and risk diversification, plus deliver top-tier services to their clientele. In this context, the application of artificial intelligence in banking cannot be emphasized enough. It greatly speeds things up here by employing machine learning alongside deep learning and natural language processing. In this article, we will explore the five main advantages of AI in the banking industry.

Artificial intelligence for banks

Artificial intelligence in banking: stats

According to Autonomous Next, AI integration in banking could save over one trillion dollars by 2030. 

A report by Insider Intelligence reveals that three out of four banks with more than USD 100 billion in assets use AI solutions for banking.

Here are some extra stats:

  • Accenture reports that AI implementation in banking has created new jobs for 83% of banks.
  • Statista identifies North America as the leading region in AI adoption in the banking sector, with a 34% market share.
  • AI will take over 15% to 25% of all banking tasks by 2034, according to McKinsey.
  • A survey conducted by PwC reveals that 52% of executives in financial services and product companies allocate significant portions of their budgets to AI investments.
  • Capgemini’s research shows that using AI for report generation saves costs for 45% of banks and insurers.

To top it all, Mordor Intelligence forecasts that the banking AI market will grow at an average speed of 23.37% per annum and reach USD 67.57 billion by 2025.

Banking AI uses

Below, you will find just a few examples of how AI transforms the banking sector:

  • Process Automation: AI software automates document processing, report generation, data analysis, and form information extraction. It handles numerous banking tasks, including loan application processing, document verification, payments, and a lot more.
  • Customer Service: Dialogue interfaces and chatbots simplify customer interactions, improving service quality and efficiency.
  • Loan Decisions: AI assesses borrowers’ creditworthiness faster and more accurately than humans do. AI credit scoring systems provide unbiased and reliable customer information, thereby enabling smarter credit decisions.
  • Fraud Prevention: Due to the rapid development of the e-commerce sector, cases of credit card fraud have increased. AI algorithms prevent credit card fraud by analyzing online transaction behavior, buyer’s location and spending patterns, and activating the corresponding security measures. 
  • Trading: AI algorithms predict stock performance by monitoring both structured and unstructured data. This way, they assist trading decisions.
  • Personalized Service and Recommendations: AI analyzes customer data and preferences, which makes it possible to offer customized financial advice to increase customer satisfaction and loyalty.

In 2024, AI helps banks create trading algorithms, optimize asset portfolios, and provide investment recommendations, thereby boosting investment returns and reducing risks. Cutting-edge AI-based technologies are used nowadays to fight financial crimes like money laundering. AI algorithms easily detect suspicious activities and lower banks’ investigation costs associated with such illicit schemes.

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Use of AI in banking: advantages

Artificial intelligence has penetrated the financial industry’s business processes more than most other sectors. This is because of the multiple benefits that such integration comes with.

1. AI for banking: boosting productivity through process automation

The use of artificial intelligence in banking and finance to routinely process applications, verify document authenticity, and analyze data reduces the sector’s reliance on manual labor. These tasks are executed much faster compared to traditional methods. As a result, banking processes become more efficient, while labor costs are reduced.

With virtual chat-bots in place, customer support services experience less stress due to work overload. Besides, voice assistants guide customers through online menus. Today, voice assistants can handle complex user queries, speeding up the customer service process and increasing its efficiency. It is safe to say that AI integration in banking boosts productivity. 

2. AI for banking: improved security in the banking sector

According to the Association of Certified Fraud Examiners, fraud costs the banking industry approximately USD 67 billion per year. With the vast number of transactions processed daily by banks, detecting instances of fraud is extremely challenging, especially if you consider the added complexities that come from online banking, social networks, and mobile applications.

Yet, with profiling, continuous auditing, and analysis of extensive datasets, we can substantially mitigate losses from fraudulent activities. Banking security departments get real-time alerts of suspicious transactions, which helps them stop fraud more efficiently.

Here’s what using AI for banking security consists of:

  • automated monitoring systems
  • biometric authentication
  • cyber threat analysis
  • access management systems
  • social media monitoring.

The benefits of AI in banking are beyond belief. Take a look at what banks can do using this cutting-edge technology:

  • improve their security systems
  • detect and prevent fraud
  • safeguard confidential customer data
  • provide customers with superior service quality.

Taken together, these measures significantly reduce losses from fraud.

3. AI for banking: smarter investment and asset management

Using AI-based technologies, banks efficiently handle big data. FYI: The latter include financial reports, economic indicators, market trends, and news updates. 

Banks actively use AI to forecast future market environments by analyzing historical data and identifying trends. This way, they adapt to evolving market dynamics, make informed decisions about asset purchases and sales timing, and effectively mitigate risks.

Here are some other ways for banks to use AI to improve their asset management:

  • Risk Management: AI helps to evaluate creditworthiness, monitor market risks, forecast potential losses, and develop strategies to mitigate them.
  • Portfolio Management: AI uses automated trading algorithms and diversification strategies to manage portfolios.
  • Investment Recommendations: AI suggests investment options based on clients’ goals, preferences, and finances.
  • Automated Trading Systems: These systems autonomously make decisions to buy or sell assets based on pre-defined criteria and strategies.

More on cutting-edge technologies in banking: International Payment Systems in 2024: Cross-Border Financial Services for Individuals and Businesses.

4. AI for banking: cost reduction potential of AI in banking and financial services

AI automation allows banks to significantly cut costs. The bank no longer needs to pay salaries to employees who previously handled such tasks as reviewing customer applications and analyzing their credit profiles. From now on, artificial intelligence successfully manages these responsibilities.

These are the ways to use AI to efficiently cut banking expenses:

  • Chatbots and Virtual Assistants: They save costs on traditional customer communication channels.
  • AI Analysis and Forecasting: Use these methods to reduce the risks of bad investments by analyzing large datasets and predicting future trends and scenarios.
  • Operational Efficiency: AI lowers the costs of maintaining and managing business processes.
  • Fault Detection and Maintenance: This is an efficient way to prevent potential downtimes.

NB: AI helps banks potentially avoid losses by improving their risk management.

5. AI in finance industry: better and faster decision-making

Using AI-based technologies, banks make smarter decisions. For them, it is an efficient way to improve service quality, streamline processes, and increase competitiveness.

By analyzing extensive datasets that include transactional records, customer information, market trends, and economic indicators, banks successfully uncover crucial patterns, trends, and correlations, which results in more rational decisions.

The benefits of artificial intelligence in banking are indeed far-reaching. In particular, AI improves decision-making policies in banks even further:

  • Machine Learning: This method is used to develop models that learn from data sets and make predictions or decisions based on the experience and insights gained.
  • Forecasting: Banks use AI forecasting models to make lending, investment, and risk management decisions.
  • Automated Processes: AI automates loan application submission and acceptance, as well as customer data processing, speeding up processes and lowering error risks.
  • Decision Support Systems (DSS): These systems analyze data, provide recommendations, and assess decision options based on set criteria and goals.

AI analyzes the risks of various operations and investments thoroughly and objectively. This technology helps make decisions that protect both the bank and its clients.

AI in banking and finance: associated risks

The use of AI in the finance industry involves risks that are largely similar to those in other sectors of the economy. Yet, we can’t but notice that the financial sector also has its own specific concerns:

  • Unpredictable System Behavior: If models are trained using incomplete or incorrect data, it may potentially result in decision-making errors, including improper fraud detection, inaccurate credit assessments, or inadequate risk management.
  • Ethical Considerations: To ensure ethical AI use, banks must address issues like data privacy, discrimination, the use of automation for critical decisions, and other ethical concerns.
  • Outsourcing Risks: Opting to use AI-based solutions from third-party providers may lead to confidentiality breaches, interpretation difficulties, and data integrity issues.
  • Technical System Dependency: Insufficient training or system maintenance is likely to result in significant operational disruptions for banks.

No matter the circumstances, banks need to make sure that their AI systems comply with the data protection laws, antitrust regulations, and other legal requirements currently in force.

Considering the rate of its development at present, the role of artificial intelligence in the banking industry is likely to become even greater in the years to come.

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