The Markov process fits into many real-life scenarios. Any sequence of events that can be approximated by the Markov chain assumption, can be predicted using the Markov chain algorithm.
Here is a business case that is using Markov Chains:
“Krazy Bank”, deals with both asset and liability products in the retail bank industry. A big portfolio of the bank is based on loans. These loans make the majority of the total revenue earned by the bank. Hence, it is very essential for the bank to find the proportion of loans that have a high propensity to be paid in full and those which will finally become Bad loans.
How does it work?
In a Markov chain, absorbing nodes are the possible end states. All nodes in the Markov chain have an array of transitional probability to all other nodes. But, absorbing nodes have no transitional probability to any other node. Hence, if any individual lands up to this state, he will stick to this node forever. Let’s take a simple example.
We are making a Markov chain for a bill which is being passed in parliament house. It has a sequence of steps to follow, but the end states are always either it becomes a law or it is ignored. These two are said to be absorbing nodes. For the loans, example, bad loans and paid up loans are end states and hence absorbing nodes.
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