Driving value with machine learning in banking

Research report


In the face of the growing uncertainty surrounding machine learning (ML) adoption in the banking industry, Itransition responds with an actionable framework.

Developed by our analysts in collaboration with technology consultants and banking leaders, the following research offers actionable insights and real-life cases of the benefits derived from machine learning in the banking industry.

It is estimated that the banking sector will lose 30% of its revenue in the two coming decades due to a multitude of factors such as competition from fintech startups. Therefore, banks need to search for new ways of conducting business more effectively. Machine learning has the potential to satisfy this goal.

In the research, we suggest the 4-step implementation roadmap to guide banks through incorporating ML, briefly summarized below.

Your 4-step implementation roadmap


Step 1.Use case identification

  • Decide on machine learning application to focus on
  • Outline your expected results

Step 2. Data gathering and preparation

  • Check your data against quality standards and privacy regulations in your region
  • Make sure the training data is diverse and free of bias

Step 3. Algorithm discovery

  • Eliminate internal risks
  • Make sure the results are explainable

Step 4. Adoption risk management

  • Communicate your vision of machine learning value to end users
  • Promote the new culture and mindset
  • Facilitate learning of new skills
  • Invest in talent acquisition

The roadmap in detail


Step 1: Identify your business case

Machine learning has many applications in the banking industry. Putting together the appropriate business case is crucial for the success of the project, as it will influence the data sets and the algorithms to be used at later stages.


Step 2: Prepare your data

Data quality is an essential part of any data-intensive project. Therefore, banks need to take the necessary precautions to make sure their data is clean and up to date. Additionally, banks need to strike the balance between deriving value from their data and maintaining customers’ data privacy.


Step 3: Understand your algorithms

Algorithms are generally considered free from bias that may result from human judgement. However, they may introduce a new form of bias springing from algorithmic settings and poorly composed training data sets.

Bank leaders can take several steps to reduce the impact of possible bias. These include defining a clear strategy for algorithm design and development, introducing assessment of training data sets, and closely monitoring all stages of the process.

Machine learning algorithms exhibit a black-box effect, meaning that their final results are not always explainable. The research suggests investing in making algorithms interpretable, depending on the business case in question and location-specific regulations.


Step 4: Help your people adapt to change

Employees’ willingness to understand and accept the new direction is essential for the success of ML projects at banks. Therefore, the human aspect cannot be taken lightly in such an initiative. These four steps will help you guide your employees through the phase of adjustment:

  1. Develop a clear vision of ML adoption and share it with the stakeholders and end users
  2. Promote the new culture around machine learning, and facilitate developing the right mindset
  3. Help employees acquire the missing skills, if any
  4. Invest in hiring new talent with skills that cannot be developed in-house

Access the full version

Click the button below to get the full version of this free report, complete with the detailed breakdown of the steps, well-informed tips, and success stories from the banking industry players.

Get the report

Takeaways


  • Assess your current situation to understand where ML can drive a tangible improvement
  • Review your current data quality, and establish long-term quality monitoring procedures
  • Select the most appropriate machine learning algorithms for your use case
  • Make sure your algorithms are interpretable and bias-free
  • Establish a ML-related vision and communicate it to your employees
  • Help your employees embrace the new ML-oriented mindset
  • Acquire the missing skills either by retraining your employees or hiring new talent