On the first day of the Association of Data Scientist’s (ADaSci) Deep Learning DevCon 2021 (DLDC), Radhakrishnan G, Head- Global Commercial and Merchant Risk Decision Science at American Express (Amex), spoke about how his company helps small businesses with real-time credit decisioning using machine learning and artificial intelligence.

Radhakrishnan is an alumnus of Management Development Institute, Gurugram. He kick-started his career as an Assistant Manager at Reliance Industries Limited before joining Amex in 2002. Throughout his almost two-decade-long ongoing stint at American Express, Radhakrishnan has been associated with risk management. His current role as the Head of Global Commercial and Merchant Risk Data Science and Risk Models across customer life cycle for card and non-card portfolios involves leading a team of more than 80 data and decision scientists across the globe. 

Leveraging ML at Amex

Radhakrishnan began his talk by introducing the audience by providing insights into the financial services company American Express. He revealed that as of 2020, Amex has 112 million cards in force, with 63,700 employees across the globe managing a worldwide billed business of $1.01 trillion and generating an annual revenue of $36.1 billion. 

• Customer service
• Customer management
• Responsible lending actions and risk decisions 
• Information management 
• Commercial underwriting 
• Loyalty marketing 

It does so with a privacy framework. 

Assessing Commercial Credit Risk

Further, Radhakrishnan talked about the dimensions for assessing commercial credit risk. He laid down the three facets for assessing risk: 

American Express leverages machine learning techniques and big technology for:

• Enhancing and managing new customer marketing
• Company profile: This includes information relating to the industry of the company, business tenure, the company’s management experience, its online presence, and public records. 
• Capacity: Under this, the company’s financial ratios (leverage and cash flows) are considered, its business revenue and limit on external trades. 
• Creditworthiness: Business credit scores, financial ratios (liquidity and profitability), historical performance on credit products and owner’s FICO. 

Radhakrishnan gave the example of the company ABC General Trading LLC seeking credit from American Express to substantiate this. Suppose the company functions in the e-commerce industry and has an income of $120,000 with an external limit of $30,000 and a debt of $100,000, with no past derogs and public records; and unavailability of revenue data. While the absence of past derogs and income of $120,000 works in favour of the company, low business tenure, involvement of large revolve behaviour on external trades, and the absence of enough data to predict its capacity to pay are con. Thus, the next ideal step would be to ask for more numbers and figure out the revenue and industry capacity. Once the bank statements are collected, it will provide American Express to get a better view of ABC General Trading LLC capacity to pay, leading to the approval for credit. However, the bank statement collection and reviewing was traditionally done manually and are a time-consuming process. This is where automating underwriting using ML and AI comes in. 

Credit Risk Models

American Express, or any other financial institution, needs AI automation to extract information from documents and ML technologies to leverage information from multiple sources to enable real-time decision making. 

• For assessing the company profile, Amex uses ML to arbitrate the best industry match. It uses name-matching algorithms to uncover related entities. 
• It uses ML for revenue estimation and optimal limit estimation. 
• Finally, it extracts information from documents using AI. For this, it uses time series intelligence using RNN (recurrent neural network). 

Filling information gaps using ML:

Industry estimation: 

Industry intelligence is an essential part of risk management, and ML techniques can help identify this with greater accuracy and speed. 

• Applicant’s company industry can be identified using industry estimation algorithms such as Smart SIC. Asking the customer or relying on the bureau is slow and tedious. Using ML, on the other hand, arbitrates between different sources based on the recency and accuracy of the source. On this, financial institutes can incorporate real-time feedback and enable business activities. This results in faster decision making. 

Revenue estimation: 

• Customer’s revenue can be estimated using a revenue estimation algorithm or Smart Revenue. This model uses data from multiple sources and makes a decision based on the recency and accuracy of the sources. 

Radhakrishnan further talks about the intelligence extraction pipeline. He says that non-standard text is cleaned up using custom text processing pipelines and Word2Vec. Furthermore, an LSTM model is used to predict transaction categories. Real-time features, along with other ML-powered features, are fed into ML models, resulting in superior decision making, more credit and faster time to market.