Mortgage lender can infuse every major decision to drive revenue, to control costs or to mitigate risks with data and analytics. Integrated risk management using risk analytics can trigger responsible and profitable growth for mortgage banks. Risk analytics in mortgage banking helps mortgage banks to identify, quantify and mitigate risks using data and analytics.
Functional areas of risk analytics in mortgage banking
- Credit risk management
- Fraud management
- Credit abuse mitigation
- Risk reporting and capital management
- Compliance management
High level steps in creating a Credit risk assessment model based on Machine Learning
- Dataset Gathering:
- Mortgage banks must aggregate and both internal and external data for creating data sets.
- These data includes personal details of the borrower such as age, employment status, income, residential status, profession and number of dependents.
- The data also includes credit history information like number and value of past loans and number and value of delinquent loans.
- Behavioral data like spending pattern and repayment patterns are also take into account.
- Target Definition:
- This step involves defining the risk metrics the machine learning model is going to assess.
- Exploratory data analysis is performed to determine these risk metrics.
- Feature or variable optimization:
- Feature or variable pre processing: This step involves cleaning the data and pre-processing the variables to match the data types so that these variables can be fed into the machine learning algorithms.
- Feature or variable selection: The next step involves selecting variables that are more predictive in nature to distinguish between dependent variables and independent variables.
- Machine learning model building and optimization:
- After analyzing and transforming variables, a small subset of data is used to build the model.
- These models are subjected to validation and testing for calculating the risk metrics using other case data.
- The results are used for optimizing and training before deploying for credit risk assessment.
Major Challenges in implementing risk analytics in mortgage industry
- Data velocity, quantity, and complexity typical of unstructured data can prove to be the major technological challenge.
- Constant changes in regulatory compliance rules.
- Managing process changes within the mortgage bank.
- User adoption of ML tools within the organization especially by users who are technically not capable to process the data at the right speed or granularity can prove to be another major hurdle.
Looking for more information on risk analytics in mortgage industry or need help in implementing the right technology, leave us a message.
