Arin Brahma, Ph.D.

Assistant Professor of Information Systems and Business Analytics, College of Business Administration | Loyola Marymount University

Los Angeles, CA, UNITED STATES

Biography

You can contact Arin Brahma at abrahma@lmu.edu.

Arin joined LMU as a tenure-track assistant professor in 2018. Prior to that, Arin served as a full-time clinical assistant professor at LMU from 2013 to 2018. Arin’s areas of expertise include machine learning, big data, operations management and supply chain analytics, and robotic process automation (RPA), with a special focus on the healthcare and financial services industries.

Arin’s research areas of interest include AI/Machine Learning in medical informatics, health IT, and financial services. Arin’s research is application-centric and incorporates Design Science Research (DSR) methods when appropriate. Arin has presented papers in top academic conferences such as ICIS and DESRIST and published research papers in top IS journals such as DSS and JASIST. Arin also contributes to the community of scholars as an Area Editor of Health Systems Journal (Taylor & Francis: https://www.tandfonline.com/action/journalInformation?show=editorialBoard&journalCode=thss20 ) and has peer-reviewed many journal papers.

Prior to LMU, Arin held many senior industry positions, founded technology start-ups and provided IT solutions as a consultant to many fortune 500 companies including Disney, Amgen, AT&T, Longs Drugs, etc. In 2019, Arin founded AI consulting company Kognivo out of LMU campus in collaboration with some of his ISBA faculty colleagues. Kognivo created many industry collaboration-based research opportunities for ISBA faculty and job placement opportunities for his students.

Arin integrates his problem-solving approach learned from his vast industry experience into his course design and pedagogy. This has allowed him to design and develop many new cutting-edge technology courses at both the graduate and undergraduate levels. Arin was also a key contributor in the design of the LMU’s flagship Master of Science in Business Analytics (MSBA) curriculum launched in 2020. Arin teaches courses such as Machine Learning, ML Model Deployment and MLOps, Big Data, Healthcare Analytics, and Operations and Supply Chain Management Analytics.

Education (4)

Claremont Graduate University: Ph.D., Data Science & Analytics 2019

Stanford University: Executive Certificate, Information Technology 2001

National Institute of Industrial Engineering, India: MS, Industrial Engineering 1987

Indian Institute of Technology, India: BS, Engineering 1985

Areas of Expertise (10)

Machine Learning

Model Deployment and MLOps

Gen AI and Large Language Models (LLMs)

Big Data Analytics

Healthcare Information Technology

Operations and Supply Chain Management

Cloud Computing

Systems Architecture and Design

Robotic Process Automation (RPA)

Ecommerce

Industry Expertise (4)

Health Care - Services

Financial Services

Retail

Transportation/Trucking/Railroad

Accomplishments (10)

Pending Patent (2023 application as principal inventor) (professional)

Design of a Generative AI Application on Private Corporate

Founder – ISBA Analytics Research Club (ARC) (professional)

College of Business Administration, Loyola Marymount University

Chair – ISBA Search and Recruitment Committee 2022 (professional)

College of Business Administration, Loyola Marymount University

Industry Interview (professional)

2021-02-13

Featured in “Meet the Expert” series of interviews by Pazanga Health Communications on the role of AI/Analytics in the healthcare space. (https://pazangahealth.com/2021/02/20/meet-the-expert-arin-brahma/

Panelist at Industry Roundtable Panel (professional)

2021-03-03

Discussion on “Data & Analytics: The Executive’s Viewpoint,” hosted by Kodiconnect (www.kodiconnect.com)

New and Innovative Solutions in Outsourcing (professional)

Speaker at 2006 SmartSourcing Conference in Universal City, CA

Emerging Market: India – Just Waiting (professional)

Panelist at 2004 Foreign Trade Conference at Cal State University, Fullerton. Panel Topic - “Emerging Market: India – Just Waiting”

India’s New Role in the Global Economy (professional)

Panelist at 2004 South Asian Business Association (SABA) Conference at UCLA Andersen School of Management. Panel Topic - “India’s New Role in the Global Economy”

Integrated E-Business Framework (professional)

Speaker at 1999 CRM One Conference in San Francisco, CA

Object Oriented Design Techniques (professional)

Speaker and Trainer at 1997 Advanced Technology Corporation in Atlanta, GA

Affiliations (3)

INFORMS
Association of Indian Management Scholars
Workflow Management Coalition (WfMC)

Event Appearances (3)

Robotic Process Automation (RPA) with AI – A New Efficiency Paradigm

AIMS International Conference Brahma A, Lontok G, Sharma S, Seal K.

2021-03-05

Comparing text mining methods for loan delay prediction

Decision Sciences Institute (DSI) Annual Meeting Zaman N, Goldberg DM, Brahma A, and Aloiso M.

2020-11-21

Predicting mortgage loan origination delays with text mining

Decision Sciences Institute (DSI) Annual Meeting Brahma A, Goldberg DM, Zaman N, and Aloiso M.

2020-11-21

Articles (6)

Are mortgage loan closing delay risks predictable? A predictive analysis using text mining on discussion threads

Journal of the Association for Information Science and Technology

Goldberg, D. M., Zaman, N., Brahma, A., & Aloiso, M.

2021-07-29

Loan processors and underwriters at mortgage firms seek to gather substantial supporting documentation to properly understand and model loan risks. In doing so, loan originations become prone to closing delays, risking client dissatisfaction and consequent revenue losses. We collaborate with a large national mortgage firm to examine the extent to which these delays are predictable, using internal discussion threads to prioritize interventions for loans most at risk. Substantial work experience is required to predict delays, and we find that even highly trained employees have difficulty predicting delays by reviewing discussion threads. We develop an array of methods to predict loan delays. We apply four modern out-of-the-box sentiment analysis techniques, two dictionary-based and two rule-based, to predict delays. We contrast these approaches with domain-specific approaches, including firm-provided keyword searches and “smoke terms” derived using machine learning. Performance varies widely across sentiment approaches; while some sentiment approaches prioritize the top-ranking records well, performance quickly declines thereafter. The firm-provided keyword searches perform at the rate of random chance. We observe that the domain-specific smoke term approaches consistently outperform other approaches and offer better prediction than loan and borrower characteristics. We conclude that text mining solutions would greatly assist mortgage firms in delay prevention.

Automated mortgage origination delay detection from textual conversations

Decision Support Systems

Brahma A, Goldberg D, Zaman N, Aloiso M

2021-01-01

For modern mortgage firms, the process of setting up and verifying a new loan, known as origination, is complex and multifaceted. The literature notes that this process is rife with delays that can stunt the firm's business opportunities, but no modern analytical techniques have been developed to address the problem. In this paper, we suggest the use of text analytic and machine learning techniques to predict likely delays. In collaboration with a large national mortgage firm, we derive a large dataset of transcripts from employees' communications pertaining to potential loans. We first use information retrieval to generate an initial list of “seed terms,” or terms most associated with loans that were delayed. We then use an array of machine learning approaches to generate predictive models based upon these seed terms. We find that these approaches are comparable in performance to less interpretable state-of-the-art approaches utilizing word embeddings. The resultant models offer interpretable and high-performing solutions to mitigate the risk of delays through early risk detection.

Designing a machine learning model to predict cardiovascular disease without any blood test

Lecture Notes in Computer Science

Brahma A., Chatterjee S., Li Y.

2019-04-27

Healthcare in the USA is struggling with alarming levels of hospital readmission. Cardio Vascular Disease (CVD) has been identified as the most frequent cause. While the factors related to high hospital readmission are complex, according to prior research, early detection and post-discharge management has a significant positive impact. However, the widening gap between the number of patients and available clinical resources is acutely aggravating the problem. A solution that can effectively identify well patients at risk of future CVDs will allow focusing limited clinical resources to a more targeted set of patients, leading to more widespread early detection, prevention and disease progression management. This in turn, can reduce CVD-related hospital readmissions. Moreover, if the patient data required by such a solution can be collected without any blood test or invasive procedure, the addressable patient population can be vastly expanded to include home care, remote, and impoverished patients while delivering cost savings of the invasive procedures. Using a Design Science Research (DSR) approach, this research has led to the design and development of a machine learning based predictor artifact capable of identifying patients with future CVD risks. The performance of this predictor artifact, as measured by the area under the receiver operating characteristic (ROC) curve, is 0.859. The sensitivity or recall is 85.9% at probability threshold of 0.5. The significant differentiating feature of this artifact lies in its ability to do so without any blood test or invasive procedure.

Understanding Cardiovascular Disease Progression Behavior from Patient Cohort Data using Markov Chain Model

ICIS 2020 Proceedings

Brahma, A., Chatterjee, S., Fitzpatrick, B., and Seal, K.

2020-11-09

Cardiovascular Diseases (CVDs) are the number one cause of deaths worldwide and management of these highly chronic diseases is a major concern to healthcare providers. Progression of CVDs often involves several comorbidities, multi-morbidities, and multiple episodic occurrences, involving recur-ring hospitalization over a period of time. Using longitudinal data of 4839 CVD episodes of 1274 real patients and continuous-time Markov model as the kernel theory, this research finds the CVD progression paths and transition probabilities. The resultant probability data and the transition paths open the door for building simulation models and tools which can help the hospital administrators to improve resource and capacity planning. Practitioners can compare a patient’s disease progression trend against the pattern revealed by the model. Results are actionable and can influence treatment and intervention strategies in overall CVD progression management by clinicians and providers. The framework developed is repeatable, reusable, and extensible to other diseases and populations.

E-Business: Beyond the Storefront

DB2 Magazine (an IBM Publication) 1999

Author

E-Business and Systems Integration Challenges

VAR Business Magazine (1999)