How Does AI in Finance Work? A Complete Guide

Artificial intelligence (AI) is modernizing the finance industry by making it easier to understand market trends, track performance metrics, learn what customers want, and even forecast future demand. AI technology in finance uses algorithms to process information in a way that mirrors human intelligence. It includes machine learning (ML), which operates by using neural networks and deep learning capabilities to learn from vast amounts of data without any programming required.

By creating and training ML and other AI models, your financial organization can quickly understand and solve problems, gain key insights that drive customer satisfaction and operational performance, help prevent fraud, and manage compliance. Learning how AI is used in finance and the ways you can incorporate it into your finance business will help you make the most out of your data to improve decision-making and overall performance.

How is AI used in finance?

Financial organizations are using AI in numerous ways and functions, from analyzing data for more informed decisions and automating workflows to increasing transparency and reducing fraud and other risks. Here are a few examples.

Anomaly Detection

AI and streaming analytics help detect anomalies in financial transactions by looking for any unusual deviations in your data. It does this by first creating a baseline pattern of normal transactions and data, then it constantly monitors and analyzes new data coming in for anomalies in the pattern. It can flag suspicious or fraudulent transactions and other atypical behaviors so you can quickly take action.

Algorithmic Trading

AI can evaluate data and make decisions faster than people, making it ideal for use in trading. You will first have to develop AI models to analyze current and historical market data so it can identify trends and predict outcomes. Once it’s trained, you can use it to make informed trades in real-time.

Sentiment Analysis

Consumer and market sentiment can change rapidly, and AI lets you stay informed and make adjustments so you don’t lose traction. Your financial organization can use AI to analyze any published texts on review sites, social media platforms, online newspapers or magazines, and chatbot data, among others, to understand how people are feeling and thinking about the market. It can help you see where things are headed next so you can make the right decisions.

Translation

With AI, you can make your financial insights and other communications more accessible to consumers. AI applications can translate financial reviews, thought leadership pieces, and pretty much any other content into other languages at scale. It can also convert speech into text. Translating your content can increase customer satisfaction and help you reach a bigger pool of customers.

Document Analysis

In addition to analyzing structured data like spreadsheets and databases, AI can also analyze unstructured data, such as text-based documents, images, social media posts, audio files, emails, and more. In the finance industry, document analysis can help speed up your loan and mortgage processing and approvals.

Predictive Modeling

AI uses current and historical data to predict future outcomes. Businesses in the finance sector build predictive models using financial data such as transactions and past trades, along with customer data. These models become more accurate with use, and they help you anticipate future risks and market to consumer trends so you can make decisions and stay competitive.

Cybersecurity

AI helps protect your financial organization from cyber threats in several ways. First, it uses streaming analytics to continuously scan your networks and systems to detect and flag vulnerabilities for you to strengthen. Second, it analyzes data and traffic to identify threats and suspicious behavior from both internal and external sources.

Data Analysis

Businesses rely on data to personalize financial services and develop successful strategies, but so much data is generated every day that humans cannot analyze it on their own. AI-powered data analytics can process and analyze vast amounts of data quickly, so you can access market and consumer insights to make better decisions.

Portfolio Management

AI optimizes investment portfolios by analyzing financial statements, economic indicators, and market data, so users can more easily track performance and improve portfolio management. It can also provide data-based advice and help anticipate and mitigate investment risks based on their financial goals.

Risk Management

Financial organizations can use AI to help identify and manage risks in several ways. As mentioned above, AI can reduce the risk of cyberattacks and detect fraud. It can also analyze financial data to assess when a customer is likely to default on their mortgage or loan to help ensure greater financial stability by working with less risky clientele. Additionally, since AI can monitor real-time data, it helps you stay in regulatory compliance.

Loan Processing

AI can assess risk in your loan process by analyzing customer data. It can also automate many loan processing tasks, such as determining credit scores, conducting risk assessments, and processing documents. AI increases efficiency and reduces manual entry errors throughout the entire process, helping you approve less risky loans faster.

10 Specific AI Use Cases in Finance (With Examples)

Here are some real-world AI examples in the financial sector so you can understand how to adopt AI in your business.

Customer Service

Companies like Wells Fargo are using an AI-powered virtual assistant to handle customer questions and support. Just Ask Fargo addresses customer questions and resolves problems about banking, investing, and other financial services 24/7 without human intervention.

These AI-powered assistants and chatbots communicate with customers in human-like conversations thanks to generative AI and natural language processing (NLP).

Detecting Fraud

ML and predictive analytics are helping banks and other financial companies detect fraud in real time. AI algorithms can predict patterns and identify unusual activity so you can take quick action and help prevent fraud. JPMorgan Chase is using AI in its credit card services to catch fraudulent transactions as they happen.

Credit Risk Assessment

Lenders like Upstart use ML and predictive analytics to automate more of the credit risk assessment process. The AI algorithms analyze current market trends and historical data and in addition to traditional credit assessment metrics to evaluate the risk of extending loans to their clients.

Wealth Management

Businesses use AI to tailor wealth management solutions for their clients by analyzing an individual’s investment goals along with their risk tolerance and assessing it against the market’s current market condition and past performance. You’ll see this reflected in BlackRock’s Aladdin platform, which provides investment insights and develops strategies based on an individual’s preferences to boost returns.

Preventing Cyber Attacks

Many financial companies use AI to help detect vulnerabilities ahead of time and reduce the threat of cyber attacks. You can train ML models to identify specific types of malware or common phishing tactics, like email spoofing, even as they change over time. It can also analyze web behavior of your users and your server activity, flagging anything suspicious to help prevent attacks.

Assessing Loan Eligibility

Banks and lenders can make smarter loan decisions with the help of AI, streamlining data analysis and automating more of the underwriting process. AI software like Ocrolus helps businesses assess loan eligibility by analyzing bank statements, income and tax documents, and more. It helps speed up the loan assessment process while also ensuring greater accuracy and compliance.

Algorithmic Trading

Trading companies can better understand trading indicators, such as economic sentiment, market trends, and trading volumes by using AI and ML. Some finance businesses also incorporate predictive analytics in addition to AI algorithms, which use historical market data to help predict future performance. You can see this in action on Virtu Financial’s AI-powered platform, which scans real-time data to enhance trade execution.

Financial Planning

Developing tailored financial planning services for your customers is easier thanks to AI robo advisors. These tools evaluate financial and market data and offer investment and financial planning recommendations by incorporating ML algorithms and analytics . Vanguard’s robo advisor helps customers build retirement savings, pay off debts, and connect with third-party accounts to give a holistic view of a customer’s finances.

Generating Reports

You can automate your financial reporting with the help of AI. It’s able to analyze big volumes of financial transactions and validate data, so you can easily generate accurate, comprehensive reports. It’s ideal for automating regulatory reports and staying in compliance. Citi has been using AI since 2019 to assist with reviewing transactions and staying in compliance.

Automation

AI helps you automate more tasks, whether offering customer support through virtual financial assistants like Erica or analyzing big data in order to provide personalized offerings to users at scale. The technology also allows you to automate repetitive tasks—including data entry or processing documents, along with reconciliation processes—which increases accuracy and efficiency across your company.

5 Key Benefits of AI in the Finance Industry

AI technology is modernizing the finance industry, helping to improve operations and overall customer experience. Below are a few benefits you can expect:

Increased Accuracy

AI-powered automation reduces your manual tasks across many different operations, including document processing for mortgages and personal loans. It can also assist with customer support, processing data, and analytics efforts. AI also helps increase the accuracy of your financial documents and data, since there’s less opportunity for human error. Plus, since ML algorithms and models naturally learn the more they’re used, they become even more accurate over time.

Optimized Efficiency

AI helps automate repetitive tasks, so your team can spend more of their time on higher revenue-producing work. The technology improves productivity and makes your operations more efficient, helping to boost your bottom line. You can even handle growth without needing to hire additional staff because AI helps you handle the workload. Calculating your AI ROI is a great way to understand its cost-effectiveness in your business.

Automated Processes

Creating more streamlined, automated processes through AI is essential in a fast-paced industry like finance. It allows you to evaluate data so your business can make informed decisions in real-time for better investment outcomes. Plus, AI helps automate processes across your business, improving your cybersecurity, speeding up your application processing, reducing fraud, and doing more to improve operations.

Quick Analysis of Large Amounts of Data

AI is capable of combining and analyzing large amounts of your financial data at a faster rate than a human can. It’s also more accurate and can detect patterns or data anomalies that people miss. With AI, you can generate real-time insights for trading and investment decisions and better manage things like compliance and risk.

Enhanced Customer Experience

Many businesses use AI-powered chatbots or virtual assistants to offer instant customer support and personalized service for a better customer experience. They can answer common questions, direct customers to helpful information on your website, or even transfer more complex situations to a customer service rep. Plus, it speeds up credit card and loan processing so customers get answers faster.

Future Trends for AI in Finance

You can expect continued adoption of AI in financial institutions in the future because it powers growth. Banks and other finance organizations can use AI-powered data analytics to understand their customers better and offer more tailored services. Additionally, more manual financial processes like loan applications will become automated, though employee oversight will still be needed. ML algorithms and other AI tools will also continue to transform trading and investment, as they can generate real-time recommendations from complex data. Human collaboration and proper governance are the key to benefitting from AI in finance now and in the future.

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