Client management and specifically  on-boarding in banking and wealth management is still a tedious and complex process. It typically requires collecting, cleaning and processing of client data, applying a number of more or less automated rules and finally some human intervention to make a final decision to flag or unflag clients.

In a earlier articles and, we went through some of the potential of machine learning in the on-boarding process, now let us go a step further and list down potential projects to enhance the client management process and specifically the on-boarding process to start with.

All the problems linked to client management belong to so called classification problems, where we want to find a way to classify information in certain buckets ( like yes or no). Typical classification problems that can be supported by well known machine learning  classification models to improve human or semi-automated accuracy, increase automation and ultimately continuously improve the accuracy of the classification problems. These algorithms are well known, do not need a lot of computing power and large amount of data like neural networks. Prototypes can be built quickly even using Machine Learning as a Service for example.

Typical projects to implement intelligence in the on-boarding process would include:

  • Prediction of prospect conversion
  • Segmentation of clients
  • Risk assessment with regards to anti money laundering
  • Sector and industry risk assessment
  • Client suitability
  • Product matching classification
  • Document and signature matching

Once the client is on boarded, we can also augment following processes with additional intelligence based on classification methods:

  • Product target segmentation
  • Continous risk monitoring
  • Churn prediction

So what is next ? A typical process can look like this:

  • Identify the problem or challenge you want to address: for example I want to better predict customer conversation rate or I want to better segment my customers for risk assessment purpose
  • Define which metrics you will be using to check if the model will be better than today’s situation: for example accuracy of conversion prediction, false positive rate, risk classification  error
  • Collect the required data, clean the data ( missing data for example)
  • Visualize the data to get a sense of what is available and to show easy to identify patterns or correlations
  • Start testing classification models ( logistic regression, random forest, decision tress and others)
  • Find the one where the metric match the defined objectives
  • Prepare findings

Do not hesitate to comment to discuss further.

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