Insight Lab

MSDA students examined a number of components reflecting the diversity of the company’s employees at all levels of the organization. They then developed a dashboard with dash and plotly to interactively visualize different measures of diversity and inclusion metrics.

MSDA students examined several business areas of the insurance industry, and collected data such as premium, incurred losses, loss ratio, experience modifications and exposures for multiple states. A Power BI dashboard was built to measure territory risk scores.

MSDA students investigated customer experience of the mortgage division of the bank in order to provided a more customized service to the clients. Machine learning techniques such as random forest, SVM and XGBoost were used in the project.

Working with a set of court documents filed in Florida state courts, the students extracted key textual information to study prosecutors’ exercise of discretion in seeking the death penalty. The students used a variety of natural language processing and machine learning techniques to classify the documents into relevant categories. They also built visualization dashboards and reporting mechanisms for the FCCR to use in analyzing future sets of court documents.

Students analyzed structured and unstructured data from different sources to predict the severity of Commercial Auto claims, enabling increased early detection of claim severity and more accurate severity predictions, which implies more investment capability. Text mining and machine learning methods such as term frequency, sentiment analysis, word2vec, logistic regression, XG-Boot and random forest were used in this project.

This project utilizes machine learning methods to understand customer attrition. In particular, students investigated historical banking transactions to identify households who are likely to attrite, distinguish those households who leave out of dissatisfaction versus normal churn (evitable vs. inevitable), and identify issues to address based on the traits that distinguish these households. Various machine learning methods were implemented.

The project develops a scoring methodology to rate and rank brokers who have done business with the company. Students used unsupervised machine learning methods such as clustering and principal component analysis to identify weights of various factors that reflect broker performance and created a broker ranking system in PowerBI.

This project explores blockchain technology. Students experimented with technologies such as Ethereum, Quorum and Hyperledger to develop a blockchain architecture to connect different parties of relevance to TSYS, perform associated analytics, set up relevant accounts in the blockchain and develop smart contracts to manage transactions. A prototype was built for deployment.

Students examine historical sales and pricing data and use machine learning to dynamically predict the optimal price for semi-commodity products. Machine learning techniques such as logistic regression, decision tree, random forest and deep learning methods such as LSTM were used to predict sales.

This project provides insight to the Better Business Bureau on understanding of causative factors that might influence or impact an individual or customers decision to do/ maintain business with the organization, through effective data analysis, application of machine learning and predictive modeling. In particular, students use both sentiment analysis and topic modeling approaches on customer review and complaint texts to explore behavior of businesses in different industries.

Students examined historical bank transactions to identify potential money laundering examples. Different machine learning methods such as decision trees, random forest, support vector machines, logistics regression, and neural networks were implemented to support anti-money laundering (AML) efforts at SunTrust.

Students analyzed various data formats at Starr and tried to automate data input process especially for unstructured texts and images. In addition, topic models such as LDA were used to summarize and classify topics in documents, and sentiments of these documents were analyzed as well.

Students analyzed unstructured data from different sources including Twitter, news media, Reddit, Facebook, and Google search trends. A system was built to systematically collect, clean, process, and analyze data that is relevant to the reputation of the city of Atlanta. Analyses included relevance filtering, topic modeling, and sentiment analyses. Machine learning algorithms were applied to improve the accuracy of classification and unsupervised learning. The results (refreshed periodically) are pushed to an online and interactive dashboard.

Students analyzed unstructured text data from legal documents and court records in order to classify lawsuit outcomes and develop a predictive model for forecasting the steps through which a lawsuit would progress and its conclusion. Methodologies used included topic modeling, Word2Vec, and various machine learning classification algorithms.

Georgia-Pacific challenged Robinson students to use images in operations to determine whether use of image recognition can detect fraud and monitor activity. Students matched same-day inbound/outbound truck images and explored the use of image data in logistics.

Robinson students were asked by SunTrust Banks to explore what website behavior, by a customer, leads to a sale and whether the bank can tailor individual interaction in real-time. During the project, students measured the impact of “visitor engagement” that increased the probability that a customer would acquire a new product.

Robinson students engaged with WestRock to improve its plant operations through image analytics. Students took pictures of corrugated boxes on an assembly line, read the labels captured in the images, and gauged the descriptions' accuracy by comparing them to the physical products. Students also took product inventory through intensity differentiation of the images.

Using Robinson’s big data lab, students used text-mining to predict client attrition for SunTrust Banks. Investigating “unstructured” texts such as underwriter’s notes, client acquisition or risk review, and sales manager’s notes from servicing clients, students provided approaches for supporting SunTrust’s goal.

To address the ongoing demand and need for blood, students set out to determine whether the American Red Cross could identify those likely to be a repeat donor and those likely to be a high value donor. Students analyzed demographic, geographic and behavioral profiles for donors and offered insights on drivers of donor loss and retention.

Students were challenged to use Starr Companies’ data on customer attributes in an existing line of business to determine what external data is useful and for what purpose in the property-casualty business. Using Robinson’s big data lab, students conducted data analysis and mining on the existing book of business to find correlations and patterns.