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 has over 20 years of executive management, client management, solution delivery, consulting, teaching, and technology experience in the field of IT enabled Services, Systems Integration and Software Development. As a passionate educator and entrepreneur, Arin is actively engaged in mentoring early stage start-up companies. Currently, in addition to running his own eCommerce venture Rebelle, he is a partner and an executive at Greendale Partners. In his previous job, Arin was Executive Vice President at Oracle's Financial Services BPO division and headed the Global Business Transformation practice. As one of the founding partners of i-flex BPO (later became Oracle's BPO unit), Arin worked in various strategic roles in growing the company from a start-up to a leading BFSI BPO company by shaping the company’s business plan, solution strategy, and customer portfolio.

Arin has worked for large corporations, successful mid-size companies, as well as entrepreneur ventures. Prior to Oracle BPO, Arin held various senior management, consulting, and technology management positions at Nexgenix, Union Carbide, Digital Equipment Corporation (DEC), Baan, and Asian Paints. During this period Arin consulted and delivered IT solutions for several Fortune 500 and large global corporations.

Arin's technology experience and knowledge is in the areas of database technologies, analytics and big data platforms and architecture, eCommerce technologies and various programming environments. Arin is currently pursuing his PhD with specialization in Data Science and Analytics at Claremont Graduate University, CA.

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 (11)

Information Technology

Management Strategies

Ecommerce

Web Development

Enterprise Software

Operations Management

Database Technologies and Analytics

Internet Marketing and Social Media

Cloud Computing

Business Process Management & Workflow

Supply Chain Management

Industry Expertise (1)

Information Technology and Services

Accomplishments (5)

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 (2)

Workflow Management Coalition (WfMC)
International Association of Outsourcing Professionals (I.A.O.P)

Articles (4)

E-Business: Beyond the Storefront

DB2 Magazine (an IBM Publication) 1999

Author

E-Business and Systems Integration Challenges

VAR Business Magazine (1999)

Author

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

ICIS2020

Arin Brahma, Samir Chatterjee, Kala Chand Seal

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.

Automated mortgage origination delay detection from textual conversations

Decision Support Systems

Arin Brahma, David M. Goldberg, Nohel Zaman, Mariano Aloiso

2020-10-31

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.