Skip to content
Tiatra, LLCTiatra, LLC
Tiatra, LLC
Information Technology Solutions for Washington, DC Government Agencies
  • Home
  • About Us
  • Services
    • IT Engineering and Support
    • Software Development
    • Information Assurance and Testing
    • Project and Program Management
  • Clients & Partners
  • Careers
  • News
  • Contact
 
  • Home
  • About Us
  • Services
    • IT Engineering and Support
    • Software Development
    • Information Assurance and Testing
    • Project and Program Management
  • Clients & Partners
  • Careers
  • News
  • Contact

Mastercard execs: Care and feeding of machine learning models is key to success

With over 2.5 billion consumer accounts, Mastercard connects nearly every financial institution in the world and generates almost 75 billion transactions a year. As a result, the company has built over decades a data warehouse that holds “one of the best datasets about commerce really anywhere in the world,” says Ed McLaughlin, president of operations and technology at Mastercard.   

And the company is putting that data to good use. The fastest growing part of Mastercard’s business today is the services it puts around commerce, says McLaughlin.

IDG’s Derek Hulitzky sat down with McLaughlin and Mark Kwapiszeski, president of shared components and security solutions at Mastercard, to discuss how the company turns anonymized and aggregated data into valuable business insights and their advice for getting the best results out of machine learning models.

Following are edited excerpts of their conversation. To hear directly from McLaughlin and Kwapiszeski and get additional insights, watch the full video embedded below.

#id61c459360cddc .jw-wrapper::before { content: “How Mastercard’s decision management platform delivers business value” !important; }

Derek Hulitzky:  Mastercard’s Decision Management Platform won our CIO 100 award in 2020.  And it uses AI and data for fraud detection. Can you tell us more about the platform?

Mark Kwapiszeski:   We use it for several purposes, primarily in our fraud products for creating things like fraud scores on transactions.  But what’s really exciting about the platform is just the size and scale and scope of what it does.  It’s built on about 900 commodity servers and it processes about 1.2 billion transactions per day at a rate of about 65,000 transactions per second, all of which it does in about 50 milliseconds per transaction. 

It uses a lot of different AI technologies and techniques; it uses about 13 different algorithms, including things like neural networks, case-based reasoning, and machine learning.  But it’s not just running one model at a time.  We’ve actually built layers, where it can run multiple models at the same time, so that it can analyze all sorts of different variables within that transaction. 

Derek Hulitzky: You’ve described how your analytics models aren’t static, and that you continuously monitor them to understand what’s happening with a transaction and why it happened.  Can you describe what you mean by that?

Mark Kwapiszeski:   When you consider every transaction that we see, every interaction, it could be fraud or it could be a mom trying to buy medicine for their child.  Every transaction matters.  So, we always have to know not only what happened, but the why behind what had happened. 

And while the models tend to get the headlines in conversations like this, to me it’s all this stuff around the model that really becomes interesting when you think about—how do you not only know what happened, why it happened, and then how do you watch that over time to watch for things like model drift. 

One of the best ways to see if you do have a model that is drifting, is by putting a challenger model in and watching it over a period of time.  And, in fact, we’ve done that for periods of upwards of a year before, watching a model, comparing it to another one, so you actually really get the best model and the best results possible. 

Derek Hulitzky: So Mark, you talked about drift. Can you talk a little bit, Ed and Mark, about how you solve for that, how you react to it?

Ed McLaughlin: I think often people almost use the wrong metaphor when they talk about AI and modeling.  They use more of a code metaphor, where you build it, you run it, and it stays fairly static until you end up end-of-lifeing it sometime down the road.  Whereas we see more with these models that need to be constantly attended and monitored. 

Mark Kwapiszeski: Yeah, it kind of manifests itself in two ways.  We have an entire analytic environment that’s really dedicated to what are those outputs and what were the results?  And then we look to marry that up with the actual end result of a transaction, because often we won’t know if an approved transaction actually turns out to be fraud until sometime later. 

So, our data scientists then take that fraud information and the signals that we’re getting, compare it back to that analytic information of what the DMP [Decision Management Platform] is putting off in the fraud scores that we have, and then they constantly then look to tweak those two things in order to find that right balance.

Ed McLaughlin: One final thing I would add, because if you want to make sure you’re not drifting, you have to be clear on your concepts.  You probably remember, just as a consumer, as a cardholder, years ago, a lot of declines, a lot of really blunt rules were out there, because the emphasis was fighting fraud.  Now, what we’re saying is … [make] sure as much good stuff gets through as it can, while you fight the fraud simultaneously. 


Read More from This Article: Mastercard execs: Care and feeding of machine learning models is key to success
Source: News

Category: NewsDecember 23, 2021
Tags: art

Post navigation

PreviousPrevious post:How to do security like GoogleNextNext post:6 Ways Data Protection as a Service Meets Your Data Challenges

Related posts

휴먼컨설팅그룹, HR 솔루션 ‘휴넬’ 업그레이드 발표
May 9, 2025
Epicor expands AI offerings, launches new green initiative
May 9, 2025
MS도 합류··· 구글의 A2A 프로토콜, AI 에이전트 분야의 공용어 될까?
May 9, 2025
오픈AI, 아시아 4국에 데이터 레지던시 도입··· 한국 기업 데이터는 한국 서버에 저장
May 9, 2025
SAS supercharges Viya platform with AI agents, copilots, and synthetic data tools
May 8, 2025
IBM aims to set industry standard for enterprise AI with ITBench SaaS launch
May 8, 2025
Recent Posts
  • 휴먼컨설팅그룹, HR 솔루션 ‘휴넬’ 업그레이드 발표
  • Epicor expands AI offerings, launches new green initiative
  • MS도 합류··· 구글의 A2A 프로토콜, AI 에이전트 분야의 공용어 될까?
  • 오픈AI, 아시아 4국에 데이터 레지던시 도입··· 한국 기업 데이터는 한국 서버에 저장
  • SAS supercharges Viya platform with AI agents, copilots, and synthetic data tools
Recent Comments
    Archives
    • May 2025
    • April 2025
    • March 2025
    • February 2025
    • January 2025
    • December 2024
    • November 2024
    • October 2024
    • September 2024
    • August 2024
    • July 2024
    • June 2024
    • May 2024
    • April 2024
    • March 2024
    • February 2024
    • January 2024
    • December 2023
    • November 2023
    • October 2023
    • September 2023
    • August 2023
    • July 2023
    • June 2023
    • May 2023
    • April 2023
    • March 2023
    • February 2023
    • January 2023
    • December 2022
    • November 2022
    • October 2022
    • September 2022
    • August 2022
    • July 2022
    • June 2022
    • May 2022
    • April 2022
    • March 2022
    • February 2022
    • January 2022
    • December 2021
    • November 2021
    • October 2021
    • September 2021
    • August 2021
    • July 2021
    • June 2021
    • May 2021
    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    Categories
    • News
    Meta
    • Log in
    • Entries feed
    • Comments feed
    • WordPress.org
    Tiatra LLC.

    Tiatra, LLC, based in the Washington, DC metropolitan area, proudly serves federal government agencies, organizations that work with the government and other commercial businesses and organizations. Tiatra specializes in a broad range of information technology (IT) development and management services incorporating solid engineering, attention to client needs, and meeting or exceeding any security parameters required. Our small yet innovative company is structured with a full complement of the necessary technical experts, working with hands-on management, to provide a high level of service and competitive pricing for your systems and engineering requirements.

    Find us on:

    FacebookTwitterLinkedin

    Submitclear

    Tiatra, LLC
    Copyright 2016. All rights reserved.