All posts by Shafi Rahman

Analytics & Optimization How Banks Can Fight Attrition and Improve Risk Predictions

Aug252015

How can banks leverage their transactional and non-traditional data sources to fight attrition and risk prediction? At this week’s Credit Scoring and Credit Control XIV conference, I will be discussing this subject in detail, but I thought I’d give you an overview of my talk. Transactional and non-traditional data sources show a lot of promise for banks. Using transactional analytics, for example, we can build more predictive behavior risk models using combination of Masterfile and transaction data. Such models are also better at predicting risk of default earlier than the traditional models. So banks can achieve the twin benefits of identifying more instances of future bad cases much earlier. Similar benefits accrue in case of attrition detection. Working with transaction data can also eliminate the need for expensive Masterfile data while keeping the performance gains intact. With the advent of Big Data technologies, it has become far easier for banks... [Read More]

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Customer Engagement How to Ensure Relevance and ROI with Discount Offers

Feb252015

In the world of customer centric targeted marketing, the biggest challenge that marketers face is that of balancing relevance with return on the investment (ROI). Relevance implies that the targeted message speaks to the targeted customer, both in terms of content as well as timing. For example, if a bank reaches out to a customer with a message about taking out a mortgage at an attractive interest rate, it would be relevant only if the customer needs the mortgage in the near future. An irrelevant message can turn off potential customers and can do more harm than good. Predicting the Right Timing This problem is a predictive problem where one not only needs to predict the likelihood that the customer would require a mortgage, but also get the timing right. Target the customer too early with a mortgage offer when she is not yet ready for the offer, and it’s... [Read More]

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Analytics & Optimization Big Data Hype and the Parable of Google Flu

May012014

Shafi Rahman In mid-March, David Lazer and his colleagues published a paper in Science that demonstrated that Google Flu Trends overestimated the number of cases of flu substantially. Google Flu Trends and other similar success stories have amplified the big hype around Big Data. After the publication, many more articles appeared taking potshots at Big Data. This is in sharp contrast to the almost juvenile euphoria about Big Data of the last two years. This negativity is as much unnecessary as the earlier hype was. Our Chief Analytics Officer Dr. Andrew Jennings has been advocating for a more balanced approach to Big Data for quite a while. He wrote in 2012 that “it is dangerous to assume that more data is automatically better than less data.” I understand that he was referring to the volume and the variety of data that is available in Big Data paradigm. Such measured approach has helped us in consolidating Big Data for better analytics.  Using our time-tested analytic development methodology, along with new ways of leveraging Big Data tools and...

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Customer Engagement Why Customer Segmentation Still Matters in the Era of Big Data Analytics

Apr152014

By Shafi Rahman In the era of Big Data analytics, it is fairly common to dismiss segmentation as an old methodology with minimal, if any, role to play in customer-centric decisioning. A majority of segmentation techniques are descriptive in nature and hence fail to capture the imagination of those who desire to use the latest techniques in modern crystal-ball gazing. That’s because people still think of relatively simple segmentation methods — in fact, there is a vital role for more advanced segmentation. Segmentation of Old The oldest technique is grouping customers based on their demographic traits. Slightly more sophisticated segmentation techniques use value dimensions instead of demographic traits. These involve identification of one or more value dimensions, followed by dividing each of the dimensions into bins, usually of equal volumes. For instance, a retailer could describe its customers by deciling them first on their yearly spend and then deciling them on total number of trips, thus giving 100 micro-segments. These techniques are relatively...

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Customer Engagement In with the In Crowd: Targeting Social Influencers

Sep262013

By Shafi Rahman The idea of social influencers is quite enticing for marketers. Mass marketing is often an expensive and blunt tool, and its impact is usually very difficult to assess. Peer-to-peer, word-of-mouth publicity is sometimes considered an effective alternative for targeting and generating desired outcomes. These marketing campaigns can be made more effective if they are seeded with individuals with high social influence, making a larger impact with lower costs. Social network platforms have opened up new opportunities for identifying social influencers. There are algorithms such as graph theory to measure node centrality and node relevance in a network. They can be applied on social networks with context specific influence metrics to identify the nodes (individuals) that are most central or relevant. The challenge is to identify whether these individuals are truly influencers, or the folks that they are supposedly “influencing” are merely similar to them. Thus the problem is how to identify the confounding factors and isolating treatment...

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Analytics & Optimization Is Your Hadoop Working For You?

Jul012013

Shafi Rahman and Eeshan Malhotra Up until the recent past, analytic scientists had to resort to complex frameworks and data designs to solve their big data problems. This was necessitated due to lack of sufficiently abstracted frameworks. The advent of modern Big Data infrastructure (note capitalization) provides the necessary level of abstraction so that the analytic scientist can focus on efficient algorithms to solve the problem without worrying about the underlying complexities. Apache Hadoop is a huge blessing. For a newbie trying to get a foot into the Big Data door though, Hadoop and the map-reduce paradigm can sometimes be quite overwhelming. MapReduce offers a unique abstraction that transcends algorithmic boundaries, but force-fitting all algorithms into this paradigm may lead to inefficient, and sometimes unintuitive algorithms. Judicious choice of Big Data technology would play a critical role in ensuring that the analytic scientist can keep their focus on the task at hand; rather than twisting and stretching their algorithm to fit into a given...

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Analytics & Optimization Analytic Scientists vs. Automatons in the Big Data World

Apr112013

A common question in today’s world is whether one can create models automatically. As I noted in one of my earlier blogs, FICO pioneered the use of Big Data and machine automation frameworks for building thousands of highly predictive models extremely rapidly by leveraging terabytes of a variety of data sources. Naturally, some folks wonder whether this is the beginning of the end of analytic scientists. On the other hand, more cautious ones are concerned about the black box nature of these automated modeling farms. It is worth noting that almost all predictive models are algorithm-driven and based on sound machine learning concepts. So training such models is easy to automate and scale to any new business problem. Simplistically speaking, one just needs to point the algorithm to the right training dataset and pass the required training parameters. Sometimes, these algorithms on their own lead to over-fitted or poorly extrapolated models. So, researchers at FICO have gone one step further and developed mechanisms to automatically identify and fix most of...

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Analytics & Optimization FICO World Preview: Mechanisms for Building Multiple Models

Feb282013

By Shafi Rahman In my session at FICO World on Mechanisms for Building Multiple Models, we will be discussing the technique of modeling automation that FICO pioneered. We have a suite of predictive modeling functionalities and architectures, which are algorithm driven and can be easily automated. They can also be used to train models in a manual, expert driven manner. A combination of algorithm and expert-driven approaches yields highly predictive models where business domain knowledge has been imputed by an expert. We have effectively leveraged modeling automation for creating an ensemble of models to solve various business problems, e.g., attrition. We begin by creating a single training dataset, which has the sampled profiles and the performance variables. The dataset is iteratively subset on rows and columns using an automated script. Each subset is used to train independent models of either the same architecture, or different architectures, again using automation. Ensemble voting techniques are then used for scoring. Modeling automation is also...

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Customer Engagement Location-Based Marketing Using GPS Data

Feb052013

Collecting GPS data is becoming quite pervasive. Using the knowledge of where a customer goes, which path she travels, and how much time she spends at various locations can improve the quality of customer interactions and types of marketing offers, and increase the likelihood that she’ll redeem an offer. GPS data is a time-series of an individual’s position information in terms of latitude and longitude. This data provides a wealth of hidden predictive information about your customers’ activity that could be used to improve marketing decisions. Working with clients, we’ve been able to gain insights on how best to leverage GPS data, including the importance of: Measuring the duration of travel vs. the duration of inactivity. We found that these were quite predictive of customers’ future behavior. We can also gain insight into customers’ life-stage and lifestyle by analyzing the time of the day they had maximum movement. Identifying the anchor points of each customer. Anchor points are places where people spend a lot of their quality time, e.g.,...

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