Introduction
Machine learning (ML) is revolutionizing the banking sector, providing banks with unique opportunities to attract new clients, enhance services for current customers, and mitigate financial risks. To thrive in this rapidly evolving landscape, traditional banks must harness the very technologies that are reshaping their industry.
With a treasure trove of historical data at their disposal, banks can leverage artificial intelligence (AI) and machine learning to gain a significant competitive edge over newer FinTech firms. Organizations that embrace these advanced tools will uncover avenues for growth, while those that resist may find themselves falling behind.
Before implementing ML solutions and utilizing existing customer data, banks must define the questions they want to address and establish metrics for success. This process involves creating a “use case.” In this guide, we will delve into the three essential qualities that characterize effective machine learning use cases, outline the primary categories of ML applications, and provide sample questions to help inspire your next project.
The Three Essential Qualities of Effective Machine Learning Use Cases
To pinpoint a valuable machine learning use case, banks must first identify the business challenges they aim to tackle. An effective use case is a succinct statement that articulates the problem at hand. Successful ML use cases share three key characteristics: clarity, relevance to a well-understood business problem, and a focus on either decisions or insights—but not both.
1. Clarity in the Use Case
An effective machine learning use case must be straightforward and comprehensible. It should be a problem that ML can solve, rather than something easily addressed by human analysis, traditional software, or standard analytics. Examples of clear use cases include:
- Will a customer remain with the bank in six months?
- Is there a risk that a specific loan will become delinquent in six months?
In contrast, less effective use cases may lack specificity. For example:
- Did consumers purchase a product due to a positive review, or would they have bought it regardless?
This question is vague, as it does not focus on a specific product or channel and attempts to tackle multiple questions at once.
2. Addressing Well-Defined Business Problems
The second hallmark of a successful machine learning use case is its focus on a clearly understood business problem. Banks should concentrate on issues that are concrete and measurable, steering clear of complex or ambiguous challenges. Common areas for machine learning applications include loans, deposits, and risk assessment—each with established key performance indicators (KPIs).
3. Focus on Decisions vs. Insights
Finally, a strong machine learning use case should prioritize either decisions or insights, but not both simultaneously. Decisions are specific and actionable, such as “Bob has a 35.41% chance of leaving the bank.” ML excels at facilitating decisions rather than generating insights. If your goal is to discover interesting patterns in data, traditional statistical methods or business intelligence tools may be more appropriate.
Ensure that your predictions enable meaningful actions. For instance, if you can identify customers likely to default on their loans, you might connect them with credit counseling services before issues escalate.
Use Case Development Worksheet
To assist you in crafting your own machine learning use case, we have provided a printable worksheet featuring targeted questions to guide your brainstorming sessions.
Printable Use Case Worksheet
Stakeholders:
Who are the key stakeholders who can help identify potential use cases and prioritize them?
Areas of Focus:
What specific business area do you wish to enhance using machine learning?
Machine Learning Opportunity:
What is the precise ML opportunity in that area? Frame it as an actionable statement, such as “detect fraud in financial transactions.”
Question to Address:
Rephrase the opportunity as a direct question.
Insights or Decisions:
Are you aiming to derive insights or make actionable decisions from your use case?
- Insights
- Decisions
Business Outcome:
What specific business outcome do you hope to achieve through addressing this opportunity?
Use Case Statement Generator:
As defined by [Stakeholder], we want to [gain insights/make decisions] concerning “[Question to Address]” to [achieve business opportunity] and enhance our [focus area] in order to [desired outcome].
Identifying Opportunities in Banking with Machine Learning
Customer Engagement
- Identify high-value customers early and tailor engagement strategies.
- Predict the likelihood of a customer moving their deposits elsewhere.
- Gauge long-term customer profitability.
- Proactively reach out to customers in financial distress.
Risk Mitigation
- Spot signs of credit deterioration early.
- Evaluate potential credit issues within your loan portfolio.
- Detect fraudulent behavior patterns.
- Establish key financial metrics indicating default risk.
Marketing and Sales
- Determine which customers are likely to purchase specific products.
- Identify high-value customers at risk of attrition.
- Score commercial leads based on risk, profitability, and likelihood of closure.
Conclusion
With the insights and tools provided in this guide, you are now equipped to initiate discussions about your bank’s machine learning project. Engage with stakeholders to explore the potential use cases for ML. What questions do you want your data to answer, and how will these insights drive your business forward?
If your team is embarking on this journey for the first time, or if you seek expert guidance to ensure a smooth process, reach out to us. Discover how we can assist you at fusionalliance.com/MLJumpstart.
