Big Data Analytics and Better Modeling Are Changing the Mortgage Industry

If you have purchased a home by taking out a mortgage, you are surely aware of how complicated the process can be. No matter how good your credit worthiness is, you may still need to go through a long and convoluted process to get a home loan. However we may be witnessing a change in the scenario, with several financial institutions looking to leveraging big data analytics to simply the mortgage application process.

How Does Big Data Change Mortgage Applications?

Conventional mortgage banking is all about modeling loan performance based on the state of the economy, the business goals of the lender, and its risk taking appetite and ability. Models have been used by risk departments to form decisions about the number of loans to be issues, the interest rates to be applied, borrowers from which economic segment and demographics would make a good borrower, and so on.

Big Data Means Better Assessments

With the entry of big data analytics, companies now have access to mountains of data – and that could be the game changer for the mortgage industry. With more data, the risk assessment models are more efficient and are able to offer more precise ideas regarding the behavior of a portfolio of mortgages. Such models are successful because they utilize a wide array of data types, including elements like the location and transaction history of a prospect, for example, and have the capability to correlate variables like comparable individuals with similar behavior.

There is also increased access to data; today, the amount of information that can be collected by an organization on one person is much more than what could be collected before. Once this data is collated, it can present a picture which you may not have imagined previously. Data sets can be of various types like credit card swipes, geolocation, special sales etc.; and they are now accessible to lending institutions and other organizations for a fee. The data in these sets can be compared and correlated with current models to facilitate improved decision making.

Today we are seeing an improvement in the type of mathematical models that are used as greater data enables enhanced tweaks and adjustments, as well as better training. To put it in short: More types of input = better decision making.

Faster Decision Making

Mortgage decisions are today being completed much more quickly, due to numerous factors. The big data based infrastructure is more powerful, and usually operates almost in real time. The big data applications use infrastructure that enable massive amounts of different types of data to be stored at less expense, and more effectively. Furthermore, easy access to historical data is made possible by such infrastructure. It also becomes easy to make the requisite adjustments in the models and validate them, allowing analysts to operate these models efficiently and speedily.

The key to these new functionalities is a computation engine that enables fast, parallel running  of models, combined with the capability to stream so that a model can be run on the fly as and when fresh data comes in.

Potential Drawbacks

Building models and greater automation are the chief ideas that have driven leveraging big data analytics for Banking and Financial applications. However, we need to be aware of the possible drawbacks:

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Becoming Impersonal

It may happen that as the models become more efficient, the decision on whether to lend to a certain applicant will be dependent more on the computer, with the human touch missing in the entire process – this has its pros and cons.

If the entire decision is left to the model, it will only consider the risk factors, and completely ignore factors that a human being may consider. Conversely, a human decision can be influenced by factors that are devoid of risk considerations. While eliminating bias can be a good thing, you also lose the ability to make judgment calls.

A good model should have two quality checkpoints – where the model suggests the answer, but the final review and decision is made by a human being.

Increased Loan Volumes

Thanks to better models and improved decision making, lending organizations may acquire an excessive risk appetite and disburse a huge volume of loans; with the increase in volume, comes the increase in risk – and that could have serious negative impacts on the institution.

Increased Chance of Fraud

There is always a certain amount of risk in allowing models to drive lending decisions. Special care needs to be exercised to factor in fraud prevention at the outset. Models have to be constructed to be smart enough to prevent gaming. Hackers may try to force certain factors to ensure a desired result. Model creators and analysts alike must consider this critical factor and see to it that the models are intelligent enough to thwart such attempts.

There is no doubt that big data analytics, when utilized properly and effectively, drives profitability: a higher number of loans closed in lesser time, and that perform better overall with a lower loss risk. As long as you handle the problem areas well, you can leverage the data to get the results that you want for your organization.