Artificial intelligence (AI) and machine learning (ML) are beginning to gain human-like intelligence and even beyond. Backed by logic and patterns, the AI brain is disrupting critical business sectors such as banking and finance. As a well-positioned AI Development Company, Oodles AI explores and discusses how artificial intelligence in finance is accelerating and improving decision-making processes for maximum growth.
In order to spread machine learning techniques even though, it is crucial to increase the ability of all the stakeholders to understand and then interpret the delivered results. This will boost the acceptance of artificial intelligence within the organizations that can use it to streamline the financial decisions, but also need the acceptance amongst regulatory bodies and the public that is affected by the decisions made by the Machine learning algorithms. The good news is that acceptance can be increased significantly by using the various approaches to make machine learning models more explainable.
Finance businesses are not only experimenting with AI within on-premise infrastructures but also in the cloud with flexible and efficient cloud machine learning solutions.
First, global interpretability will help us to understand the functioning of the model as a whole. Global interpretability involves providing an answer to questions such as-
For Example, when should use machine learning in fraud prevention and detection, features such as a device’s metadata, user account data and personal data such as a user’s age can be taken into consideration.
Each of these features can have different importance when building a model. At the same time, the interaction of these features within the model may result in a completely different format. For example, while user age could be of minor importance when used as a single variable by itself. However, the combination of user age and device data can be an interesting feature that can be integrated into a machine learning model for fraud detection.
Secondly, local interpretability is one of major importance on a local level. If a given user is surprised to receive goods and services only against the prepayment, due to him being classified as a potential fraudster, checking each of the feature characteristics of the model against the customer’s data and behavior helps us to explain how and why this result came about.
Even though interpretability can be a crucial factor for the success and acceptance of machine learning models, it can not be always considered in the conception phase for a model. What is our experience with explainable machine learning? Do we use machine learning models in our organization? or How are we tackling these challenges? Such questions need to be clarified.