In today highly competitive environment, hiring and maintaining an internal data science team is increasingly difficult. At Kuber Financial, LLC see this as an opportunity to build a team of best in class mathematicians and statisticians to help our clients to improve their key performance indicators.

We’ve build multiple generation of probabilistic based algorithms that will you to generate more leads, convert more customers and at the same time maintain your prepayment rate and default rate.

Our off-the-shelf statistical models have helped many new and existing lenders to launch new products quickly and learn quickly.

We are experts at building custom solutions for many of our partners and clients. Based on our partners individual needs and specialized datasets, we have created multiple highly specialized models to deliver specific results with respect to our clients preferences. We believe that a model tuned specifically to our clients needs is one of the most cost effective way to turn your business around.

We are crunching millions of transactions and constantly improve the aims of our statistical models. We employ a tick-tock method where every two campaigns or every two months, we will retrain our models for our clients to further improve their key performance indicators. When the models improve with a constant learning process, our clients win.

Statistical Modeling

Whether our models are targeting prospects, ability to pay or profit margin, we spend the majority of our time to comb through millions of records to quality control the dataset. We believe even a slight amount of error in model target definition could result in an underperforming model.

We employ a variety of algorithms. Depending on our clients needs and situation, we design and implement rules, linear regression algorithms, machine learning algorithms and artificial neural network algorithms. We also use ensembling approach where we allow each model to vote as a group and seek consensus.

We monitor client’s model performance daily. We believe that models can be statistic and that models need to be retrained, rebuild to suit the ever changing business environment. Model governance includes model performance monitoring. If a model starts to deteriorate, it’s time to rebuild or recalibrate the model.

Choose an approach that’s right for you

Prediction vs. Accuracy

When we approach any model building process. We need to understand the model’s intent and its usage. If a client is asking for a fraud detection model, accuracy would be our focal point. Where as if the client wants to use a model to predict prospect customers, we focus on prediction power.

Risk vs. Response

We often have to balance between risk and response for our clients. We often look for highest response population from the entire prospect universe, however those that have the highest response characteristics are also highly likely to miss a payment or default on their entire financial obligations all together.

Recalibrate vs. Rebuild

We recommend that a model needs to be constantly rebuilt. With today’s changing environment both in marketing channels and consumer behaviour.  Your models must be retrained, recalibrated and rebuild. In the past few decades, because the computational cost was too high, banks can’t afford to rebuild models often. However, with today’s technology, that constraint is no longer there, allowing us to reprocess a massive amount of data in a matter of minutes.