Developing a recommendation engine using a multitiered algorithmic approach tailored for analyzing different levels of data, channel, and industry (ex: association analysis, Support Vector Machine (SVM)) and Bayesian network. Usage of Machine Learning and Deep Learning methods are implemented for the financial services industry which adopts a multi-model system. The recommendation engine is built by using collaborative filtering of the data using different algorithms and recommends the most relevant suggestions to the users by capturing the past behavior of the customers/users who share similar likes. Such algorithms are used for the following:

  • Algorithmic trading
  • Portfolio Management
  • Fraud detection
  • Loan/insurance underwriting
  • Risk management
  • Chatbots
  • Document analysis
  • Trade settlements
  • Prevention of money laundering

Conclusion: Financial Services industry adopts multi-model recommender Systems that are built on linear, neural networks, and deep-learning techniques, they can assess risks associated with customers, disburse loans and provide other financial services within milliseconds to help the industry in enhancing cross-selling opportunities and improving financial inclusion.