While more and more machine learning models are being experimented, there are still few challenges that needs to be still addressed: Bias: Data Bias might exist in the historical credit decision data that is used for training the model drawn from decades of bias that existed in housing and lending markets. Algorithm bias where Individuals who are building the AI systems might also knowingly or unknowingly can build bias in the algorithm. Blackbox: Dramatic increases in complexity of the algorithms have led to black boxes that are often vulnerable to risks, such as accidental or intentional biases and errors, thus raising the question of how to “trust” algorithmic systems. Operationlisation of models: It is estimated that more than 80% of the models in the experimentation phase are not going into production due to complexities in the deployment of models.