The field of banking and finance is highly regulated, with a broad range of laws and regulatory compliance requirements to consider. This reality complicates the deployment of new technology. But most will find that the potential benefits — and ROI — far outweigh the challenges of implementing new technology.
Artificial intelligence (AI) and machine learning use cases in finance and banking abound, there are countless examples of how process automations, “robo advisors” and forecasts are transforming the industry.
Investment and Stock Market Predictions Using AI and Machine Learning
“Alas, if we could only predict the stock market!” Well, now you can with the help of machine learning-powered artificial intelligence technology. Stock market forecasts generate predictions using machine learning to examine historical data.This data is subsequently used to develop and train a machine learning algorithm that will drive artificial intelligence predictions and forecasts.
Machine learning-driven AI can be used to predict stock prices, which, in turn, enables investment professionals to make data-driven trading decisions. This includes algorithmic trading, whereby AI and machine learning technology is used to identify trends and patterns. Trading strategies are developed using this data and insights. There’s also high-frequency trading, or HFT, which entails the rapid identification and execution of trading opportunities.
Financial Process Automation Use Cases
The financial sector works with an exorbitant amount of data. Much of that data is extremely important, but processes such as data input and data processing are tedious and time-consuming. Enter: process automations.
Process automation empowers financial professionals to off-load data entry and data processing tasks from its human resources, allowing them to focus on more engaging tasks. This improves employee morale, leading to better staff retention levels.
Robo-advisor Use Cases in the Financial Industry
Robo-advisors are used to offer investment advice to users, helping them make data-driven decisions as they establish and maintain an investment portfolio. Robo-advisors collect information from the user to determine their investment objectives and the ideal level of investment risk. This information is considered using AI technology, which then makes recommendations for stocks and other investments that align with the user’s goals.
Robo-advisors are a prime example of machine learning-driven AI technology in the financial space, giving investors the insights they need to make well-informed, data-driven investment decisions.
Risk Management, Security and Fraud Detection Using Machine Learning-Powered AI
The financial sector’s risk management equation is a complicated one. The financial industry is inundated with security threats and fraud is a very real problem for financial institutions. Fortunately, machine learning-powered artificial intelligence technology is very adept at identifying patterns and trends. This technology is an essential component of financial industry risk management strategies.
When a particular pattern arises or there is a departure from a well-established trend, it may be indicative of a security breach. This empowers a financial institution’s risk management / security team, enabling them to take action sooner rather than later. Quicker intervention serves to limit losses.
Anomalous account activity may be indicative of a security breach. To limit losses, a bank can configure its system to rapidly lock accounts and notify account holders when specific anomalies are observed.
Machine learning-powered AI technology can also be used for detecting insider trading, money laundering and other illegal activities that are confronting financial institutions.
Machine Learning Use Cases for Lending Opportunities
An increasing number of financial institutions are leveraging machine learning technology to help evaluate loan applications and to provide borrowers with information on what lending options are available to them based upon their financials and credit scores. This technology can be used to calculate credit scores and evaluate an individual’s financials such as income. Then, the system can generate a report with information on the available lending options.
Finding the Best Machine Learning and AI Development Partner
AI and machine learning use cases in finance are many and varied. At 7T, we specialize in Digital Transformation development for banks and financial institutions, with machine learning and AI technology that brings a robust ROI for our clients. By working with a top Dallas Digital Transformation development company that’s experienced in the newest emerging technologies like machine learning-powered AI, you’ll maximize your chances of success with quantifiable KPI metrics and ROI.
The Digital Transformation development team here at 7T is guided by the approach of “Digital Transformation Driven by Business Strategy.” As such, the 7T development team works with company leaders who are seeking to solve problems and drive ROI through Digital Transformation and innovative business solutions such as multimodal machine learning-powered AI implementations in the financial industry.
7T has offices in Dallas, Houston and Austin, but our clientele spans the globe. If you’re ready to learn more about machine learning and AI development solutions for the financial space, contact 7T today.
