The goal is to turn data into information, and information into insight – by Carly Fiorina
Data, data everywhere but not a defined strategy to predict!
People say data will talk to you if you are willing to listen. And who understands it better than our data experts.
Predictive Models as the name suggests, are the models that help us in estimating future outcomes based on historical data. It uses data, statistical algo and machine learning techniques.
The intent is to go beyond knowing what has occurred to provide the best assessment of what will occur in the future.
But all of this is easier said than done.
In this blog post, we bring you top 5 real challenges faced in developing predictive models. So, let’s get started.
Often, experts are unable to estimate the usefulness of data and use inappropriate data (past data) to predict the future. So, understanding the real worth of data is quintessential.
Too many or fewer data sets; this is one problem that every data scientist faces. The initial hurdle may be how do we use, extract, clean or interpret the data, to acquire significant insights and build models from them.
Dig deep into your data. Always use curated and clean data. Dig deep into the data and entirely understand it to evade any predicaments at later stages of modelling.
Most of the data in the financial /mortgages industry are unstructured. They are in the form of scanned documents, pdfs, images etc. To harness the real value of these datasets we need to employ sophisticated AI solution to convert them into an accessible and structured dataset for any meaningful analysis and building cutting-edge machine learning applications.
The process ends with electronic closing, which saves borrowers the difficulty of having to meet a closing agent in-person, enables them to review the closing documentation at their own time and discuss any concerns. This zeroes down the chances of delays caused by a last-minute glitch in the documentation.
Identifying the problem statement is the first step, followed by identifying the required data and collection methodology. For example, to estimate the overall mortgage risk for underwriting purposes, you may need a better customer profile. Credit bureau data combined with social footprints can be used to better understand the customer behaviour and this can significantly impact overall decision making with respect to overall risk and frauds.
As the famous story goes about Amazon’s predictive model, that was used to predict employee success, favoured males. Only because the model used the company’s employee history, and most of the employees were male. This is an illustration of data bias.
A comparable problem often creeps in when you are building predictive models. There are numerous ways to circumvent data bias in predictive models - by adding a human touch to your model, diversifying your team, balance and build the model yourself and ALWAYS deploy dummy data before implementation.
For example, in the mortgage underwriting process, an inclination of prejudice towards or against a person, object, or community can be disastrous for any mortgage provider and may attract regulators attention. Challenges of data bias while building AI applications needs to be handled with caution
This situation arises when a given model is performing well on the training data, but the performance dwindles significantly over the test set - known as overfitting model.
On the other hand, if the model is functioning poorly over the test and the train set, then it is an underfitting model.
A model that neither underfit nor overfit, is considered the best.
For any mortgage provider, it becomes crucial that the deployed models are optimal. For instance, an overfitted mortgage risk estimator may not generalize well in a real-world scenario and may start making a wrong decision affecting the bottom line, and an under fitted estimator may not be even able to estimate the risk appropriately.
Discordance between the data used and the model built. Model Evaluation is an indispensable part of the model development process. It accommodates to find the most suitable model that describes our data and how considerably the chosen model will work in the future.
Model Evaluation is a continuous process. AI solutions for mortgages also need to be evaluated and recalibrated periodically. For example, AI-based solutions for assisting underwriters in their initial stages may learn from the human-interventions until they achieve a significant level of performance and to monitor this learning process periodic evaluation is critical.
In completion, predictive analytics impersonates numerous challenges. But the business advantages are tremendous.
Tell us your top challenges in Building Predictive Models.
