While you may be hesitant to adopt artificial intelligence (AI) and machine learning for finance, your competitors likely aren’t. According to a recent survey of finance executives, over half of businesses (57%) have already adopted AI and machine learning use cases in finance—and even more are considering it essential for the future. Despite the challenges related to adopting AI, companies that fail to adopt it miss the competitive advantage it offers in efficiency and strategic decision making. Here’s how your competitors are using AI and machine learning right now.
Machine Learning in Finance Turns Tedium Into Value Creation
Machine learning and deep learning are two types of artificial intelligence that identify patterns and relationships in data, improving over time as they analyze more information— much like a human would. But unlike us, machine learning algorithms can analyze vast data sets from disparate sources in near real-time—think financial reports, customer profiles, supply chain logistics, market data and more. This processing ability allows AI to unearths trends and other insights with unprecedented levels of accuracy, granularity and speed.
Instead of spending time on tedious data processing, humans can use the constant flow of insights to improve business processes, solve complex problems and enhance decision making for today and tomorrow. But make no mistake: to create value, this technology requires human judgment, particularly the ability to validate AI outputs with real-life business and industry acumen.
The Top Machine Learning Use Cases in Finance, According to Research
Respondents of the survey revealed three machine learning applications in finance that stand out as early wins: predictive analytics and forecasting, process automation and personalization. Here’s what those use cases can look like in your business.
1. Predictive Analytics & Forecasting
The majority (67%) of businesses are applying machine learning in finance to anticipate opportunity and disruption so they can improve decision making. In fact, the vast majority of businesses are using AI and machine learning for strategic long-term planning.
Businesses are maturing their data analytics to understand not just what happened, but what will happen—and with much greater accuracy.
Various analytical functions are elevated with machine learning tools:
- AI-driven revenue forecasting and growth simulations can analyze client metrics, leads and other sales data to predict future performance.
- Businesses can scale production and optimize pricing based on AI-generated demand forecasts, which incorporate projected geopolitical developments, supply chain analyses and other economic factors to reduce financial risk.
- AI enables tighter cash flow optimization through advanced predictive and prescriptive analytics.
- Scenario planning, previously an annual exercise, can leverage AI to rapidly stress-test and solve for the most probable or impactful scenarios on a quarterly basis.
- Businesses can identify patterns in customer or employee behavior to predict churn and proactively mitigate loss.
2. Process Automation
More than half (56%) of surveyed companies are using machine learning in finance for process automation to streamline internal workflows and cut costs by reducing paperwork, manual effort, human error and risk.
Adopters can using machine learning automation in a number of ways:
- AI can improve cross-enterprise collaboration with automated reporting, automatically aggregating and analyzing data from various sources to save time and effort with AI-generated visualizations and flash reports.
- Machine learning provides continuous anomaly detection in accounting processes by identifying deviations from normal patterns. This flags potential errors or fraud for human review while minimizing false positives.
- Machine learning can be used to automate invoice processing by extracting relevant data, categorizing expenses and even making approval decisions for regular, predictable expenses.
- Businesses can automate bank reconciliations, identifying discrepancies faster and with greater accuracy than manual methods.
- Combined with natural language processing (NLP), machine learning tools can extract, analyze and organize text from varying sources like contracts, invoices, internet forums and chats. These tools can also serve as chatbots for customer service queries. NLP is the #1 application among small corporations, used by over 60% of businesses with revenues under $10 million.
3. Personalization
From chatbots to recommendation engines, personalization constitutes the number one machine learning use case in finance for large companies, adopted by over 73% of corporations with revenue over $250 million.
How adopters are using it:
- AI-driven tools can design better collection models to recover 5–20% more top-line revenue from delinquent accounts.
- Machine learning algorithms can analyze customer data including purchase data, behaviors, demographics and more to better segment customers, guide personalized offers or optimize pricing.
- Nearly half (44%) of adopters are applying machine learning for HR activities. These could include defining skill gaps, recommending allocations and proposing hires based on industry trends, future needs and graduation data.
Barriers to Machine Learning Applications in Finance
For the 42% of businesses that lag behind adoption, implementation is not a question of why, but how. More than eight in 10 leaders (83%) agree it’s important, but concerns about talent, data security and the loss of human oversight stand in the way.
- Talent shortage: More than half of senior leaders (53%) reported a lack of advanced analytic skills followed by a lack of data governance and technology proficiency in requisite tools and software (38% for each).
- Cybersecurity: Machine learning applications in finance require the use of data, and a lot of it—a difficult endeavor for organizations with poor data cultures. More data from more sources increases your surface area for attack, not to mention the liability of acquiring, storing and using customer or partner data.
- Cost and lack of transparency: These two roadblocks were nearly neck and neck (33% and 35% respectively), suggesting that many organizations view machine learning solutions as black boxes—likely due to an inability to build, explain and interpret them clearly.
Leveraging machine learning for forecasts or automation will require businesses to think differently about the finance function. For the above use cases to be most effective, CFOs and finance leaders will need to navigate new talent models that combine full-time and fractional finance talent with engineers and data scientists in order to create value quickly and build models that serve finance teams effectively. Businesses will need talent that can skillfully manage data and explain AI outputs to key stakeholders, ultimately helping teams build trust in their AI solutions.
The Future of Machine Learning in Finance
As machine learning use cases in finance advance from task-based to ideation-based tools, foresight and human intervention become vital.
To win in the future, businesses must approach with:
- A commitment to data quality: Ensuring complete and ethical data sets by challenging the models and understanding what assumptions the model is making.
- Awareness of liability, intellectual property and licensing: The days of AI models training on free data scraped from the corners of the internet are numbered. You will need lawyers, technologists and future-forward CFOs who can work together to evaluate risk, adjudicate ambiguity and decide on which data investments to make as more people and organizations start insisting on monetizing the data they generate.
There are two initial steps to adoption: assessing needs and assessing readiness. Book a consultation with Paro today to catch up with adopters on the journey to mastering machine learning in finance. Our fractional experts can help your business address financial and analytical skill gaps to position your business for AI success.
