Finance is a leading contender for the title of the most data-reliant industry. Most mission-critical operations in financial firms, institutions and departments hinge on the successful capture, organization, analysis and report generation for vast quantities of data they handle daily. Each of these core functions has historically been performed by individual data analysts and other staff members who leveraged software that simplified their jobs and made it easy to save their work for future reference. This opens up a number of machine learning use cases in finance.
Finance companies can leverage machine learning technology, which can be integrated into software platforms in an effort to automate many of these vital processes. Such platforms can also streamline and organize workflows to divide the workload evenly. This spares highly skilled employees from repetitive tasks and allows them to focus on work that requires human discretion instead of being bogged down with data capture and rote organizational tasks.
This article discusses several common applications for machine learning platforms in the financial space, as well as use cases and case studies that depict more concrete examples of what custom machine learning software development can help an organization achieve.
Examples of ML Use Cases in Finance
Machine learning has the potential to deliver multiple benefits to financial institutions, firms and finance departments within other firms. These benefits become clearer when viewed through the lens of use cases for machine learning in finance, as listed below.
Machine Learning Use Cases in Finance
Use Case
Details
Logistics / Process Automation
Many repetitive tasks with a consistent set of steps don’t require much individual discretion. Such tasks can benefit from process automation with AI to increase speed and consistency. Leveraging AI-powered automation allows the tool to learn over time and get better at finding the necessary information to use for basic tasks without direct programming.
Transaction Data Management
ML technology can collect and analyze the massive volumes of data that financial institutions process in countless daily transactions. This analysis offers critical insights that can improve and streamline operations.
Automated/Semi-Automated Underwriting
Machine learning tools can store, analyze and even help with decision-making for incoming credit applications based on pre-existing criteria. Using a machine learning model with the ability to self-improve over time can reduce risk and maximize return. This technology can also be configured to auto-trigger manual reviews if certain criteria aren’t met, avoiding costly errors.
Regulatory Compliance
ML can assist compliance departments by automating the analysis that’s required to maintain compliance with the many regulations in the banking and finance industry.
Cybersecurity and Fraud Detection
Machine learning-powered tools can learn from manually reported cases to identify key markers indicating fraud, enabling them to catch fraudulent transaction attempts almost instantly. Promptly alerting a bank’s security team to suspicious activity improves the security of online assets and transactions.
Risk Management
By tracking and analyzing client information, ML can make recommendations based on real-time data to help banks properly manage risk while market factors — such as interest rates and default risk — change in the background.
Customer Experience
ML tools allow customers to access basic banking functions outside regular banking hours via convenient online interfaces or AI chatbots. Such tools can also assist customers with account setup and loan originations for well-qualified or simple credit applications.
Automated Market Research
By enabling a machine learning-powered tool to monitor market trends continuously, firms can surface real-time insights for investment opportunities.
The benefits of applying machine learning to these finance industry use cases are fairly straightforward:
- Faster, more accurate, more cost-efficient administrative work;
- A more flexible and adaptable business model;
- Better data consistency;
- Better service experience for clients/customers;
- Improved risk management; and
- Higher productivity for employees supported by AI tools.
Such advantages can present new investment opportunities, which lead to better returns for financial institutions and the clients and customers who rely upon their ability to provide financing and interest on investments.
Case Study: Commercial Real Estate
Dottid came to 7T with one goal in mind: To apply dramatic innovation to the antiquated leasing process. Dottid is the only platform that allows building owners and managers to view transactions with complete transparency and easy communication, alongside their leasing team and tenants. No more messy paper trails or unnecessary games of phone tag.
Dottid Financial Mobile App Capabilities
Feature
Details
Connected Teams
Thanks to Dottid, everyone involved in a real estate deal can track their progress and communicate with one another instantly.
Optimized Digital Leases
The days of paper leases are officially over. Dottid allows users to review and sign leases digitally – no printing or scanning required.
Greater Transparency
A well-executed commercial lease involves several players. Dottid makes sure everyone is on the same page throughout the process from start to finish.
Results
Dottid offers real estate professionals a number of benefits, including:
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- Powerful Workflow Management;
- Greater Broker Retention;
- Connection for All Team Members;
- Improved Tenant Visibility;
- Top-Down Visibility;
- Cloud-Based Security;
- Enhanced Deal Tracking; and
- Lead Time Metrics and Analytics.
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Using machine learning in the commercial finance industry improves operational efficiency and protects businesses from unnecessary risk.
Experience the Benefits of Machine Learning With 7T
At 7T, our development team is on the cutting edge of machine learning software development. Machine learning use cases in finance can offer greater efficiency, better margins, more accessible services and more manageable employee workloads. For all of these reasons, we pride ourselves on the results that we are able to deliver for our finance clients
Our world-class team of Dallas AI developers will work to identify challenges within your organization; then, we’ll create a value-generating solution with innovative technologies that align with your business strategy. This problem → solution approach to AI development is the key to our clients’ success.
7T is based in Dallas, with additional locations in Houston, and Charlotte, NC. But our clientele spans the globe. If you’re ready to learn more about the use of generative AI in finance, with a look at how this technology can benefit your organization within the financial space and beyond, contact 7T today.
