Finance Industry Improved with AI Innovations
The use of artificial intelligence (AI) in the finance industry is revolutionizing the way we interact with money. From credit decisions to quantitative trading and financial risk management, AI is helping banks and other financial institutions to streamline processes, optimize decision-making, and meet the demands of consumers who are looking for smarter, safer, and more convenient ways to access, spend, save, and invest their money.
With the market value of AI in finance projected to reach $9.45 billion in 2021 and grow by 16.5 percent by 2030, it is essential to know the key players in this growing industry. In this article, we will examine the areas where AI is gaining momentum in finance and highlight some of the companies that are leading the way.
AI in Financial Credit Decisions
AI has revolutionized financial credit decisions, providing lenders with more accurate and effective ways of assessing credit risk. With traditional credit scoring models often failing to capture the complexities of modern lending, AI-powered underwriting platforms are increasingly being used to make smarter credit decisions.
Several companies are leading the way in this field. For instance, Enova uses AI and machine learning in its lending platform to provide advanced financial analytics and credit assessment. The company aims to serve non-prime consumers and small businesses and help solve real-life problems, like emergency costs and bank loans for small businesses, without putting either the lender or recipient in an unmanageable situation.
Ocrolus offers document processing software that combines machine learning with human verification. The software allows business, organizations and individuals to increase speed and accuracy when analyzing financial documents. Ocrolus’ software analyzes bank statements, pay stubs, tax documents, mortgage forms, invoices and more to determine loan eligibility, with areas of focus including mortgage lending, business lending, consumer lending, credit scoring and KYC.
DataRobot provides machine learning software for data scientists, business analysts, software engineers, executives and IT professionals. DataRobot helps financial institutions and businesses quickly build accurate predictive models that inform decision making around issues like fraudulent credit card transactions, digital wealth management, direct marketing, blockchain, lending and more. Alternative lending firms use DataRobot’s software to make more accurate underwriting decisions by predicting which customers have a higher likelihood of default.
Scienaptic AI provides several financial-based services, including a credit underwriting platform that gives banks and credit institutions more transparency while cutting losses. Its underwriting platform uses non-tradeline data, adaptive AI models and records that are refreshed every three months to create predictive intelligence for credit decisions.
Zest AI is an AI-powered underwriting platform that helps companies assess borrowers with little to no credit information or history. The platform uses thousands of data points and provides transparency that helps lenders better assess populations traditionally considered “at risk.” The company reported that auto lenders using machine-learning underwriting cut losses by 23 percent annually, more accurately predicted risk and reduced losses by more than 25 percent.
Underwrite.ai uses AI models to analyze thousands of financial attributes from credit bureau sources to assess credit risk for consumer and small business loan applicants. The platform acquires portfolio data and applies machine learning to find patterns and determine the outcome of applications.
Socure created ID+ Platform, an identity verification system that uses machine learning and AI to analyze an applicant’s online, offline and social data, which helps clients meet strict KYC conditions. The system runs predictive data science on information such as email addresses, phone numbers, IP addresses and proxies to investigate whether an applicant’s information is being used legitimately. Socure is used by institutions like Capital One, Chime and Wells Fargo, according to its website.
These companies are just a few examples of how AI is helping financial institutions rethink the underwriting process and make more informed and accurate credit decisions.
AI in Personalized Banking
AI is transforming personalized banking by using data analytics to provide a more customized and convenient experience for customers. By analyzing customer data, AI-powered assistants can provide personalized financial advice and self-help customer service, including chatbots and voice assistants. Here are a few examples of companies using AI in personalized banking:
- Kasisto – Kasisto has created KAI, a conversational AI platform used to improve customer experiences in the finance industry. KAI helps banks reduce call center volume by providing customers with self-service options and solutions. Additionally, the AI-powered chatbots also give users calculated recommendations and help with other daily financial decisions.
- Abe.ai – Abe AI is a virtual financial assistant that integrates with Google Home, SMS, Facebook, Amazon’s Alexa, web and mobile to provide customers with more convenient banking. The assistant provides services ranging from simple knowledge and support requests to personal financial management to spending overviews and budgeting. In 2016 Abe released its smart financial chatbot for Slack. The app helps users with budgeting, savings goals and expense tracking.
- Trim – Trim is a money-saving assistant that connects to user accounts and analyzes spending. The smart app can cancel money-wasting subscriptions, find better options for services like insurance, and even negotiate bills. Trim has saved more than $20 million for its users, according to a 2021 Finance Buzz article.
- Wells Fargo – Wells Fargo’s AI-powered chatbot, named “Ella,” provides personalized financial advice and self-help customer service. Ella can answer questions about accounts and transactions, provide spending insights and budgeting tools, and even help customers find a nearby ATM or branch location.
- Capital One – Capital One’s AI assistant, “Eno,” uses natural language processing to understand and respond to customer inquiries. Eno can help customers manage their accounts, provide real-time transaction alerts, and even detect and prevent fraud.
Overall, AI-powered personalized banking is changing the way customers interact with their financial institutions. By providing personalized advice and self-help services, these AI assistants are making banking more convenient and efficient for customers.
AI in Quantitative Trading
AI has revolutionized the way quantitative trading is carried out in the financial industry. With the ability to analyze large and complex data sets, AI-powered computers can quickly and efficiently identify patterns and make strategic trades. This type of algorithmic trading saves valuable time and helps financial institutions make better trades.
One example of a company that uses AI in quantitative trading is Canoe. Canoe is a data management platform that collects and extracts alternate investments data, such as documents on venture capital, art and antiques, hedge funds and commodities. Its platform uses natural language processing, machine learning and meta-data analysis to verify and categorize a customer’s alternate investment documentation. Canoe’s system allows traders to quickly identify trends and patterns in the data, enabling them to make more informed investment decisions.
Another example is Entera, an AI-powered investment platform for real estate investors. The platform allows investors to buy, sell and operate single-family homes through its SaaS and expert services. It also enables investors to access homes from on and off-market sources. Additionally, Entera can discover market trends, match properties with an investor’s home and complete transactions.
AlphaSense is an AI-powered search engine for the finance industry that serves clients like banks, investment firms and Fortune 500 companies. The platform utilizes natural language processing to analyze keyword searches within filings, transcripts, research and news to discover changes and trends in financial markets. AlphaSense is valuable to a variety of financial professionals, organizations and companies — and is especially helpful for brokers. The search engine provides brokers and traders with access to SEC and global filings, earning call transcripts, press releases and information on both private and public companies.
Kavout uses machine learning and quantitative analysis to process huge sets of unstructured data and identify real-time patterns in financial markets. One of Kavout’s solutions is the K Score, an AI-powered stock ranker. The K Score analyzes massive amounts of data, such as SEC filings and price patterns, then condenses the information into a numerical rank for stocks. The higher the K Score, the more likely the stock will outperform the market.
Alpaca is another company that uses AI in quantitative trading. It uses proprietary deep learning technology and high-speed data storage to support its yield farming platform. (Yield farming is when cryptocurrency investors pool their funds to carry out smart contracts that gain interest.) Alpaca is compatible with dozens of cryptocurrencies and allows users to lend assets to other investors in exchange for lending fees and protocol rewards.
Overall, AI has played a significant role in improving the efficiency and accuracy of quantitative trading in the financial industry. As the market continues to evolve, it is likely that more companies will adopt AI-powered solutions to better manage their investments and generate higher returns.
AI in Financial Risk Management
Risk management is a critical component of the financial sector. The ability to accurately identify, assess, and mitigate risks can mean the difference between success and failure. In recent years, AI has become an essential tool for financial institutions looking to improve their risk management capabilities.
One of the key advantages of AI is its ability to analyze vast amounts of data quickly and accurately. This capability makes it well-suited for risk management, where large data sets must be analyzed to identify patterns and predict future risks. Additionally, AI can be used to automate many of the tasks involved in risk management, freeing up human resources to focus on higher-level tasks.
Here are some examples of how AI is being used in financial risk management:
- Fraud detection: One of the most significant risks facing financial institutions is fraud. AI-powered fraud detection systems use machine learning algorithms to analyze transaction data, identify patterns of suspicious activity, and alert risk managers to potential fraud.
- Credit risk management: AI can help financial institutions manage credit risk by analyzing borrower data, such as credit scores, income, and employment history. Machine learning algorithms can identify patterns that are predictive of default and flag high-risk loans for closer scrutiny.
- Market risk management: AI can help financial institutions manage market risk by analyzing market data and identifying trends that may indicate potential risks. Machine learning algorithms can be used to predict changes in market conditions and help financial institutions adjust their portfolios accordingly.
- Operational risk management: AI can be used to identify potential operational risks by analyzing data from a wide range of sources, including social media, news articles, and regulatory filings. Machine learning algorithms can identify patterns that may indicate emerging risks and alert risk managers to potential issues.
- Cybersecurity risk management: As financial transactions increasingly move online, cybersecurity risk has become a significant concern for financial institutions. AI-powered cybersecurity solutions use machine learning algorithms to analyze network traffic, detect potential threats, and respond in real-time to prevent cyber attacks.
Overall, AI has the potential to revolutionize financial risk management. By providing financial institutions with the ability to analyze vast amounts of data quickly and accurately, AI can help identify emerging risks and mitigate potential losses. As AI technology continues to evolve, we can expect to see even more advanced risk management solutions that will help financial institutions stay ahead of the curve.
AI in Cybersecurity and Fraud Detection for Banking
Every day, millions of digital transactions take place as users move money, pay bills, deposit checks, and trade stocks online. As online transactions continue to increase, the need to ramp up cybersecurity and fraud detection efforts is now a necessity for any bank or financial institution. The challenge for financial institutions is how to detect fraudulent activities in real-time and respond before it’s too late. AI plays a key role in improving the security of online finance and detecting fraud in real-time.
Here are a few examples of companies providing AI-based cybersecurity solutions for major financial institutions:
- Vectra AI Vectra offers an AI-powered cyber-threat detection platform, which automates threat detection, reveals hidden attackers specifically targeting financial institutions, accelerates investigations after incidents, and even identifies compromised information. Its solution is based on real-time detection and response to cyberattacks and provides a comprehensive view of the entire network. Vectra’s platform uses AI algorithms to detect and analyze patterns of abnormal behavior, identify advanced persistent threats (APTs), and respond with real-time alerts.
- Jumio Jumio’s KYX platform helps businesses establish trust with online customers. The platform validates customer identity with facial recognition, screens customers to ensure they are compliant with financial regulations and continuously assesses risk. Additionally, the platform analyzes the identity of existing customers through biometric authentication and monitoring transactions. Jumio is commonly used in education, healthcare, retail, and gaming industries.
- F5 Utilized by top banks in the United States, F5 provides security solutions that help financial services mitigate a variety of issues. The company offers solutions for safeguarding data, digital transformation, GRC, and fraud management, as well as open banking. F5’s solutions help financial institutions protect against cyber threats and data breaches, while also ensuring compliance with regulatory requirements. Its application security solutions use AI to detect and prevent attacks in real-time, allowing financial institutions to stay ahead of cyber threats.
- Darktrace Darktrace’s AI and machine learning platform analyzes network data and creates probability-based calculations, detecting suspicious activity before it can cause damage for some of the world’s largest financial firms. Bank One implemented Darktrace’s Antigena Email solution to stop impersonation and malware attacks, according to a case study. The bank saw a rapid decrease in email attacks and has since used additional Darktrace solutions across its business.
- FIS FIS provides a host of banking and financial solutions. One way it uses AI is through a compliance hub that uses C3 AI to help capital markets firms fight financial crime. Announced in 2021, the machine learning-based platform aggregates and analyzes client data across disparate systems to enhance AML and KYC processes. FIS also hosts FIS Credit Intelligence, a credit analysis solution that uses C3 AI and machine learning technology to capture and digitize financials as well as delivers near-real-time compliance data and deal-specific characteristics.
In conclusion, the use of AI in cybersecurity and fraud detection has become a vital tool for financial institutions. With the increasing amount of digital transactions, AI-powered cybersecurity solutions can help financial institutions detect and respond to fraudulent activities in real-time. By leveraging AI, financial institutions can ensure that their customers’ financial data is protected and that their institution remains compliant with regulatory requirements.
In conclusion, artificial intelligence is transforming the banking and finance industry in numerous ways. From credit decisions to risk management, quantitative trading, personalized banking, and cybersecurity, AI has brought significant improvements to the sector. Companies are leveraging machine learning, natural language processing, and other AI technologies to streamline processes, reduce costs, and improve accuracy. The market value of AI in finance is expected to grow significantly in the coming years, indicating that the industry will continue to rely on these technologies.
AI has also brought benefits to consumers, providing more convenient and personalized banking experiences. While there are concerns about the impact of AI on jobs and the potential for biased decision-making, the benefits of AI in finance are undeniable. As technology continues to evolve, we can expect to see even more advancements in the financial sector.