Benefits of Machine Learning for consumer lenders to detect risks and fraudulent activities

With increasing incidents of payment defaults and alarming credit risk rate, consumer lending companies realised the challenges of managing credit risk. This has prompted most lending companies to build risk mitigation models using Machine Learning, so as to reduce the risk of default while taking smart and well informed lending decisions. Traditional analytical techniques of assessing the borrowers profiles has turned obsolete owing to manual processing of socio-demographic data combined with increased complexities with vast data available. Current Machine Learning algorithms can process this data with deeper data analysis and help lenders derive insights about their existing and potential borrowers. This in turn helps the lenders to disburse loans to the right set of borrowers.

Massive amount of data is generated on a daily basis related to the human activities in the form of online and offline, structured and unstructured, aggregated and non-aggregated. This data is obtained from smartphones, mobile wallets and social media activities can uncover information about customer spending habits, travel plans, work details, leisure activities, etc. this extensive data can be processed to gain insights and learning of the borrower which can shed enormous light on an individual’s willingness to repay, likelihood of default, ability to pay, etc. This data can also be used to track any kind of fraudulent activities or instances of money laundering.

Lending companies can leverage Machine Learning to transform consumer lending operations by setting up strong checkpoints to ensure secure lending practices. With access to consumer’s credit history and other relevant data, technology can aid lending companies to determine exact level of risk associated with any particular borrower. Machine Learning models can effectively predict high risk customers which alerts the risk and collections teams allowing them to take suitable actions effectively. Furthermore, ML models can investigate and bring to fore suspicious activities at a customer level. Recent improvements in Machine learning models are now allowing lenders to take effective action against defaulters and drive awareness by educating borrowers.

Needless to say, Models build using Machine learning are empowering consumer lending companies to strengthen their fraud and risk management practices. Lenders adopting to such models will eventually be able to strategize their operations effectively, strengthen their customer acquisition, deliver better experiences and control credit losses.

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