If there’s one area that’s looking to go all in on Artificial Intelligence it’s the finance sector, and for good reason.
Machine learning technologies are already being used by about 70% of all financial service companies surveyed.
The applications for AI in financial services are vast. Everything from AI-powered Forex trading to real-time fraud detection or credit analysis.
In fact, more than 50% of banks surveyed said they are relying heavily on AI for fraud detection, optimizing IT operations, and digital marketing.
In this article, we’ll take a look at real-world examples of where AI is being used in the finance and banking sectors.
What Are Some Real-World Applications of AI In Finance?
The real-world applications of finance are all around us and chances are you’ve interacted with an artificial inteligence algorithm without even knowing it.
Let’s take a closer look at the companies in the finance industry that are implementing AI and machine learning in the finance sector to optimize productivity, safety, and their bottom lines.
1. Blackrock’s Alladin
Asset, Liability, Debt, and Derivative Investment Network (Alladin) believe it or not has been in development since 1988 by Blackrock – the largest asset manager in the world. Aladdin leverages massive data processing capabilities and advanced machine learning algorithms to assess investment risks and opportunities in the finance industry.
Aladdin allows analysts and portfolio managers to analyze and manage investment risks across various asset classes. For example, a risk-management specialist could asses how a specific portfolio would perform under a hypothetical economic scenario – a job that would be incredibly difficult, if not impossible for humans.
Aladdin is offered as a service to large financial institutions and asset managers to support and influence their investment planning activities. Currently serving over 200 clients, Aladdin has fuelled BlackRock’s growth by diversifying revenue and tightening connections with big financial players.
Aladdin’s advanced machine learning algorithms and extensive data processing capabilities demonstrate a powerful marriage between artificial intelligence and finance.
2. Enova’s Colossus
Enova is a financial technology firm that strategically leverages its proprietary analytics model to drive business profitability in the finance industry. This responsive model adjusts marketing expenditures in line with demand fluctuations and modifies credit thresholds, thereby ensuring optimal business performance.
Since its inception in 2003, Enova has collected over 22 terabytes of customer data allowing it to significantly improve the capabilities of its machine-learning model.
By using predictive models, pattern recognition, and machine learning, Enova optimizes lending decisions and reduces risk, ensuring the company remains profitable.
3. NumerAI One Fund
Numerai is a unique AI cryptocurrency project that is conducting a real-time data science tournament to predict the stock market with machine learning.
Numerai’s data science tournament incentivizes experts and data scientists to try and predict the stock market, in return, they are rewarded with NMR tokens – the native cryptocurrency that fuels the Numerai ecosystem.
Leveraging artificial intelligence algorithms, Numerai has the ambitious goal to become the world’s most powerful hedge fund. Since Numerai’s inception in 2019 the Numerai One fund, which charges the investment firms fees, has returned 38%.
Lemonade is bringing insurance into the 21st century with generative artificial intelligence technologies and behavioral economics.
The key difference in Lemonade’s business model is the speed it can process and settle claims – usually within a number of minutes via its mobile app. This fast turnaround time is handled entirely by the company’s backend AI system, and Lemonade is planning on implementing further generative AI for as many as 100 of the company’s business processes.
Lemonade’s long-term goal is a Growth-loss ratio of less than 75% – which is about where a well-run insurance company will operate. In Q1-23 Lemonade’s growth-loss ratio sat around 87%, down from 94% in Q3-22, which looks great for the company’s generative AI future.
5. Scienaptic’s Ether
Scienaptic is a company that specializes in providing credit underwriting solutions in the financial sector through its AI-powered platform, Ether.
Founded in 2014 and headquartered in New York City, Scienaptic AI aims to enhance credit decision-making by leveraging machine learning and data analytics.
Ether, the company’s flagship platform, helps financial institutions make more informed credit decisions by analyzing vast amounts of data. Customers can use the platform for various credit products including credit cards, personal loans, and mortgages.
Using AI and machine learning, the Ether algorithm aims to improve approval rates by 25-35% while simultaneously reducing credit risk to lenders.
Scienaptic has been recognized as an innovator in the fintech space and has formed partnerships with various banks and financial institutions.
6. Bank of America’s Erica
Erica is a virtual assistant provided by the Bank of America that helps customers to stay on top of their finances with natural language processing and AI.
Erica is accessible through the Bank of America’s mobile app and helps users with a range of services, such as checking account balances, scheduling bill payments, providing credit report updates, as well as giving smart notifications and alerts.
Erica also offers personalized financial guidance by analyzing spending patterns and giving insights to customers.
Erica is one of many chatbots implemented in the finance industry. Some others include Amelia by IPsoft and Wells Fargo’s Wells Assistant.
AlphaSense harnesses AI to optimize financial research and decision-making.
Their AI search technology quickly surfaces insights across multiple sources, including historical documents, industry reports, and expert calls.
Alphasense features such as Smart Synonyms and Trending Topics enable users to find information as fast as possible, and real-time alerts keep them updated. AlphaSense’s AI capabilities also facilitate sentiment monitoring over time, aiding in investment decision-making.
This AI-driven financial intelligence search engine saves users significant time by conducting comprehensive research and providing centralized access to essential data.
What Are the Generalized Applications of AI in Finance?
1. Algorithmic Trading
Algorithmic trading involves using AI algorithms to make trading decisions at speeds that would be impossible for humans. These algorithms analyze massive datasets and execute trades at optimal times to maximize profits. Trading bots consider multiple variables including market trends, historical data, and current events, and can make millions of trades in a single day. High-frequency trading (HFT) is a notable subset of algorithmic trading.
2. Fraud Detection
AI systems are widely employed in the financial industry for fraud detection. These systems can analyze massive amounts of transaction data in real time, identifying unusual patterns that might indicate fraudulent activity. They constantly adapt and learn (reinforcement learning) from the data, which enhances their accuracy over time.
3. Customer Service
Many financial institutions now use AI-powered chatbots and virtual assistants to handle customer inquiries. These natural language processing tools can respond to questions, process transactions, and provide financial advice. By handling routine queries efficiently, they help in reducing operational costs for financial institutions and improve customer satisfaction. Oftentimes, modern AI-powered chatbots are more efficient and effective than dealing with human customer service.
4. Personalized Finance
AI plays an essential role in providing personalized financial services. By analyzing data on customers’ financial history, preferences, and behavior, AI offers tailored financial advice and investment recommendations. It can also help users to budget and manage their finances more effectively, leading to more informed financial decisions.
5. Loans and Lending
AI is employed to assess the creditworthiness of applicants by analyzing diverse data sources like social media activity, transaction history, and employment information. AI models help financial instiutions make accurate risk assessments and help to streamline the loan approval process. AI-powered lending has also been shown to reduce discrimination in the lending process.
In the insurance sector, AI is used to customize policy pricing, streamline claims processing, and detect fraudulent claims. By analyzing data such as customer profiles, historical claims, and market trends, AI can offer personalized insurance products. It also speeds up claims processing by automating the process.
7. Risk Management
Risk management is critical in finance and AI helps in making it more effective. Using AI algorithms, companies can predict potential risks by analyzing market conditions and historical data. This allows them to take preemptive measures to mitigate risks, thus protecting their assets and ensuring stability.
8. Regulatory Compliance (RegTech)
Regulatory compliance, often referred to as RegTech is an area where AI helps financial institutions to comply with laws and regulations. AI can analyze regulatory texts, monitor transactions, and ensure that the operations of an organization operate within legal frameworks. This helps in reducing the risk of fines and legal issues.
9. Portfolio Management (Robo-Advisors)
AI-powered robo-advisors have become popular for managing investment portfolios. By analyzing market trends and investor preferences, robo-advisors automatically allocate, manage, and optimize client assets. This often results in lower fees compared to traditional human advisors, and in some cases has been shown to result in better performance.
10. Sentiment Analysis
AI’s sentiment analysis capabilities are used to gauge market sentiment from various media sources. By processing vast amounts of unstructured data, AI can provide insights into public opinion regarding particular assets or the market as a whole, which can be invaluable for making predictions and educated investment decisions.
11. Financial Forecasting
In financial forecasting, AI analyzes historical data and market trends to make predictions about future market movements and asset prices. Machine learning models can generate more accurate and timely forecasts, helping investors and companies make better-informed decisions.
12. Blockchain and Cryptocurrency
AI is also being integrated with blockchain technology and cryptocurrency trading. It helps in optimizing trading strategies, securing blockchain transactions, and analyzing the cryptocurrency market. AI’s ability to process large datasets can be particularly useful in tracking and understanding the volatile crypto markets. Public ledger analysis is an emerging area in the AI blockchain space.
13. Cash Flow Management
For businesses, AI can be instrumental in optimizing cash flow management. By analyzing historical financial data, purchase orders, and market conditions, AI can provide insights and forecasts on cash flow trends. This helps businesses in better planning and resource allocation, ultimately driving profitability.
Artificial intelligence is transforming the finance industry in countless ways. From algorithmic trading to robo-advisors to fraud detection, AI enables financial institutions to operate more efficiently, minimize risk, and provide better services.
As AI capabilities continue to advance, we can expect even more innovative applications in finance. Though still in its early days, it’s clear that AI will be instrumental in shaping the future of finance.