techinfi
Machine learning can significantly improve the predictive power of models that measure customer creditworthiness, affordability, and fraud. ML models offer even more advantages.
Machine learning can lead to a more accurate assessment of “good” and “bad” customers. Ultimately, this allows companies to increase revenues and reduce operating costs. However, any company using ML must ensure the results are fully explainable.
Otherwise, they risk fines and a possible loss of reputation.
The improved predictive power of ML models
One of the biggest benefits of machine learning models is the improvement in predictive power. For example, the Gini coefficient, used to assess the effectiveness of credit risk models, can be increased by an average of 20 per cent compared to conventional models without machine learning.
The more predictive or accurate the models are, the more profit can be made from a better credit risk assessment.
The availability of new data sources combined with the power of cloud computing makes ML even more attractive for companies. ML model performance tends to improve when more data is available to train a model.
When assessing credit risk, using advanced analytics to leverage and analyze large amounts of data has allowed organizations to include more data sources and variables in their decision-making logic.
A good example is the use of transaction data from open banking, which provides a much greater depth of information for credit risk modelling.
Classification of transaction data into different categories
Using machine learning, the transaction data is divided into different income and expense categories. In this way, they can easily flow into the modelling phase, where ML can be used to develop an individual score.
In this way, ML can use alternative data to assess better customers whose dataset was previously considered “too thin”, providing more customers access to services and promoting financial inclusion.
Machine learning can lead to better predictions and decisions in a very short time by analyzing large amounts of data, both structured and unstructured, and nonlinear relationships in the data.
In this way, companies can largely automate their decision-making. This reduces operating costs and enables faster decision-making without manual checks. The thus shortened “Time to Yes” leads to a better customer experience and thus to more sales.
Obstacles to machine learning adoption
Despite the obvious benefits, many companies still need to be convinced to use machine learning. A study described a lack of explainability as the biggest barrier to ML adoption.
In particular, companies have concerns because ML use for credit decisions is still in its infancy. In addition, companies are held back by a need for more internal knowledge and know-how.
According to Gartner, this lack of expertise has meant that only about half of projects involving artificial intelligence methods go live. Companies must find and use tools to build and deploy ML models without investing heavily in data scientists and developers.
Companies get a very functional analytical environment and, if necessary, continuous employee support.
Ethical and legal reservations about machine learning
Another potential obstacle to using machine learning is fundamental doubts about whether it is ethically and legally justifiable. ML systems largely learn independently using data, and with many algorithms, the causal relationships in the resulting models are not obvious.
That is why people often speak of a “black box” whose results are not transparent and cannot be explained. This is often unacceptable, especially when making credit, affordability and fraud decisions.
Today there still needs to be perfect technical methods to grasp the explainability. The legal and data protection requirements are still open in some places and are difficult to define.
Nonetheless, there are tools to develop stable and legally compliant AI systems that create added value, are understandable and secure, and align with our social values.
Fraud prevention solution
In the current situation, without clear legal or regulatory requirements for using machine learning, companies should meet their responsibility through such independent tests.
This applies out of self-interest so that any objections are taken from the sails from the start. ML models are ready to go.
About Author
