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.
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.
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.
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.
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:
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