Financial institutions try to predict defaulting customers by analyzing previous transaction data. To reduce the number of defaulting customers, the financial institution may suggest default prevention initiatives to customers.
- One month late
- Two months late
- Three months late
- Four months late
Use all transaction sequences ending with Paid to train one model, and all sequences ending with Defaulted to train a second model. Evaluate the current sequence of statuses for a customer against both models. Base the default prediction on the model that gives a higher probability.
The hidden states of the HMM inherently indicate the financial health of the customer. A hidden state with a high probability of emission of defaulted status indicates poor financial health, while a hidden state with high probability of emission of paid status indicates good financial health.
The observation symbols to build the HMM are the statuses referenced by number in the order shown in the following table.
|one month late||3|
|two months late||4|
|three months late||5|
|four months late||6|