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“Not just GenAI” Leadership Series — Part III

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“Not just GenAI” Leadership Series — Part III

Your team is planning to build an AI/ML model for predicting who will default on their borrowed loan.

The historical dataset has several rows, one row per borrower, where Features/Characteristics of the borrowers are captured. These are called the independent variables. The target/dependent variable is also captured — namely, Defaulter (with values Yes or No).

For the sake of simplicity and ease of explanation, let’s just go with 2 features – Employment Status and Amount.

So, for each borrower, you have the employment status and amount values, and the target variable will indicate whether the borrower has defaulted or not. These are all historical/factual data.

Your team plots all these points on a graph, with one point representing one borrower (obviously, the Employment Status and Amount become the 2 axes of the graph).

Your team’s goal is to draw a line on the chart, that separates the defaulters from the non-defaulters in the ‘cleanest’ manner. Cleanest here means, one side of the line should have mostly defaulters, and the other side, mostly non-defaulters. Sure, perfectly clean groups on either side is ideal, but highly unlikely, since you are using a straight line.

Again, there are infinite lines that can be drawn on this graph, but the goal is to draw one that gives you the cleanest separation of the 2 classes — Defaulters and Non-Defaulters.

Deciphering Loan Approval: Linear Discriminants in AI/ML Analysis

Once a new person applies for a loan, your team collects the info about Employment status and loan amount, and plots that on the graph. Depending upon which side of the line the new applicant falls – defaulter side or non-defaulter side — your team can take the decision on whether to give the loan or not.

The job of any AI/ML algorithm would be to arrive at that line/model. 

Since these models distinguish or discriminate between 2 or more classes, they are called Linear Discriminants. 

2 important points:

  1. Again, this is a very simplistic example with just 2 variables. In reality, there will be a large number of variables considered for such modeling. 
  2. Also, practically speaking, your team does not need to plot such graphs (indeed it is impractical when there are several variables). Instead, they will resort to ML code/tools for this analysis. But this explanation should give an idea about what happens under the hood.

Discriminants are an example of Discriminative AI/ML that we referred to, in the previous posts. Another type of Discriminative AI/ML algorithms, are called Logical Discriminants. Decisions Trees and their variations are examples of such Logical Discriminants.

Unraveling Decision Trees: Splitting Data for Loan Approval

Decision Trees and their ensembles/variations are one of the most widely used algorithms in AI. Let’s use the same business example as above, for understanding Decision Trees.

The Decision Tree algorithm’s job is to figure out which variable — Employment status or Amount — can be used to split the data into 2 clean groups — Defaulters and non-defaulters. 

Important: For the Linear Discriminant model above, you had to consider both the variables Employment Status and Amount, together, for building the model. Whereas in the case of Decision Trees, you would consider these variables one at a time. 

Let’s say the data is split into 2 groups based on Employment status. In other words, the variable Employment Status is used to discriminate the data into 2 groups (hence the name Logical Discriminants). Sure, you will very likely not get 2 clean groups. The group of Employed folks, for example, will likely have both defaulters and non-defaulters (hopefully more of the latter). 

Now the algorithm will take each of the above 2 groups and further divide them based on the second variable, Amount. The group where Amount is greater than some value will have Defaulters and non-defaulters, So also the other group. 

Basically, the job of the Decision Tree algorithm is to use each variable to keep breaking the data into Defaulters and non-defaulters, thus forming a tree, a Decision Tree.

When a new person applies for a loan, he/she is placed into the appropriate group based on the values of Employment Status and Amount. If that group happens to be the non-defaulter group, great, the person is given the loan.

Summary

A Linear Discriminant creates one single divider line/plane to segregate the data into relatively clean classes, using a single numeric formula.

A Logical Discriminant (e.g. Decision Trees) will break the data into logical groups based on the values of the variables, till it gets near-clean groups. 

Both these are examples of Discriminative AI/ML. They focus on the boundaries of the classes. The question that they ask is — What is the probability of getting a particular value of the target variable, given a set of values of the independent variables? That is, they look at Conditional Probability, as discussed earlier.

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Jayaprakash Nair

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Michael Woodall

Chief Growth Officer of Financial Services

Michael Woodall, as the Chief Growth Officer of Financial Services at Altimetrik, spearheads the identification of new growth avenues and revenue streams within the financial services sector. With a robust background and extensive expertise, Michael brings invaluable insights to his role.

Previously, Michael served as the Chief of Operations and President of the Trust Company at Putnam Investments, where he orchestrated strategic developments and continuous operational enhancements. Leveraging strategic partnerships and data analytics, he revolutionized capabilities across investments, retail and institutional distribution, and client services. Under his leadership, Putnam received numerous accolades, including the DALBAR Mutual Fund Service Award for over 30 consecutive years.

Michael’s dedication to industry evolution is evident through his involvement with prestigious organizations such as the DTCC Senior Wealth Advisory Board, ICI Operations Committee, and NICSA, where he served as Chairman and now holds the position of Director Emeritus. Widely recognized as an industry luminary, Michael frequently shares his expertise with various divisions of the SEC, solidifying his reputation as a seasoned presenter.

At Altimetrik, Michael plays a pivotal role in driving expansion within financial services, leveraging his expertise and Altimetrik’s Digital Business Methodology to ensure clients navigate their digital journey seamlessly, achieving tangible outcomes and exponential growth.

Beyond his corporate roles, Michael serves as Chair of the Boston Water & Sewer Commission, appointed by the Mayor of Boston, and is actively involved in various philanthropic endeavors, including serving on the board of the nonprofit Inspire Arts & Music.

Michael holds a distinguished business degree from Northeastern University, graduating with distinction as a member of the Sigma Epsilon Rho Honor Society.

Anguraj Kumar Arumugam

Chief Digital Business Officer for the U.S. West region

Anguraj is an accomplished business executive with an extensive leadership experience in the services industry and strong background across digital transformation, engineering services, data and analytics, cloud and consulting.

Prior to joining Altimetrik, Anguraj has served in various positions and roles at Globant, GlobalLogic, Wipro and TechMahindra. Over his 25 years career, he has led many strategic and large-scale digital engineering and transformation programs for some of world’s best-known brands. His clients represent a range of industry sectors including Automotive, Technology and Software Platforms. Anguraj has built and guided all-star teams throughout his tenure, bringing together the best of the techno-functional capabilities to address critical client challenges and deliver value.

Anguraj holds a bachelor’s degree in mechanical engineering from Anna University and a master’s degree in software systems from Birla Institute of Technology, Pilani.

In his spare time, he enjoys long walks, hiking, gardening, and listening to music.

Vikas Krishan

Chief Digital Business Officer and Head of the EMEA region

Vikas (Vik) Krishan serves as the Chief Digital Business Officer and Head of the EMEA region for Altimetrik. He is responsible for leading and growing the company’s presence across new and existing client relationships within the region.

Vik is a seasoned executive and brings over 25 years of global experience in Financial Services, Digital, Management Consulting, Pre- and Post-deal services and large/ strategic transformational programmes, gained in a variety of senior global leadership roles at firms such as Globant, HCL, Wipro, Logica and EDS and started his career within Investment Banking. He has developed significant cross industry experience across a wide variety of verticals, with a particular focus on working with and advising the C-Suite of Financial Institutions, Private Equity firms and FinTech’s on strategy and growth, operational excellence, performance improvement and digital adoption.

He has served as the engagement lead on multiple global transactions to enable the orchestration of business, technology, and operational change to drive growth and client retention.

Vik, who is based in London, serves as a trustee for the Burma Star Memorial Fund, is a keen photographer and an avid sportsman.

Megan Farrell Herrmanns

Chief Digital Officer, US Central

Megan is a senior business executive with a passion for empowering customers to reach their highest potential. She has depth and breadth of experience working across large enterprise and commercial customers, and across technical and industry domains. With a track record of driving measurable results, she develops trusted relationships with client executives to drive organizational growth, unlock business value, and internalize the use of digital business as a differentiator.

At Altimetrik, Megan is responsible for expanding client relationships and developing new business opportunities in the US Central region. Her focus is on digital business and utilizing her experience to create high growth opportunities for clients. Moreover, she leads the company’s efforts in cultivating and enhancing our partnership with Salesforce, strategically positioning our business to capitalize on new business opportunities.

Prior to Altimetrik, Megan spent 10 years leading Customer Success at Salesforce, helping customers maximize the value of their investments across their technology stack. Prior to Salesforce, Megan spent over 15 years with Accenture, leading large transformational projects for enterprise customers.

Megan earned a Bachelor of Science in Mechanical Engineering from Marquette University. Beyond work, Megan enjoys playing sand volleyball, traveling, watching her kids soccer games, and is actively involved in a philanthropy (Advisory Council for Cradles to Crayons).

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