In a Nutshell: A lender’s ability to accurately assess creditworthiness is crucial to its success. Miscalculating a borrower’s risk can lead to lost profits and serious trouble for the lending institution. But with Anaconda, lenders can access rich data sources and use machine learning to more accurately assess borrowers. This enterprise data science platform enables companies to go beyond simply looking at FICO scores and examine alternative data sources, to provide a more accurate view of today’s consumer. Anaconda’s platform can be scaled to fit companies of any size.
Let’s say you’re walking down the street and a man walks up to you and asks if he can borrow $100, and he adamantly promises that he’ll pay you back.
You would likely tell him to take a hike, right? You don’t know the man and have no reason to loan him money, much less a way of knowing that he would actually pay you back.
But what if the man walked up to you, asked to borrow $100 — but he was accompanied by one of your best friends who said he’s a stand-up guy and always pays his debts. And, the man offers to pay you back with 10% interest within a month.
You may be likely to consider this scenario since your friend vouches for him and you could actually benefit from the transaction. But if the man flakes out on his debt, you’re out $100.
This is a small-scale version of the types of decisions banks and lenders make thousands of times every day. Determining the creditworthiness of potential borrowers is crucial for the success of the business. If a lender makes too many miscalculations and its borrowers aren’t repaying their debts, the business loses money.
That’s why lenders have always taken the task of measuring risk very seriously.
Anaconda is an enterprise data science platform that incorporates cutting-edge machine learning (ML) tools so lenders can build models that help make those all-important decisions about which borrowers are creditworthy.
Anaconda’s Director of Technical Services, Michael Grant, said the company was founded on the idea of building products to enhance the Python data science ecosystem. Python is a coding language that lends itself well to data science applications.
“Their original intent was to identify some commercializable products that could make the use of data science much more productive,” Grant said. “The founders discovered that one of the number one problems that needed to be addressed was simply package management and package distribution.”
With the development of Anaconda, the founding team filled a need in data science that existed across numerous industries, including IT, financial services, retail, energy, and more.
How Anaconda Takes Lenders Beyond FICO Scores to Assess Risk for Modern Consumers
“Determining a customer’s creditworthiness holds many complexities,” according to the Anaconda website. “Lenders have to decide whom to approve for credit, at what interest rate, and through which products and services.”
Historically, these types of considerations have been largely based on FICO scores — and often still are today. But, according to Anaconda, FICO scores are limited because they do not take a lender’s own customer data into account.
“This hurts lenders in multiple ways, including the risk of denying qualified customers, qualifying credit card churners, and losing business to competitors because of slow and inaccurate responses,” according to the website.
Grant said applying Anaconda to help analyze creditworthiness was an obvious use case for the platform within financial services.
“We’ve helped customers move models from closed-source proprietary platforms into Python, and satisfy the regulators and their risk management officers at the same time,” he said. “But there are also other benefits like fraud detection. And using natural language processing for automated invoice scanning and categorization.”
The company understands that the way consumers manage their credit today has evolved. That’s why Anaconda encourages lenders to move beyond FICO scores to examine alternative data sources to asses risk using machine learning.
“For customers with limited traditional credit history, such as many millennials, lenders can evaluate credit risk based on digital footprints,” according to the website. “An effective AI/ML strategy can help lenders draw deeper insights about customers — thereby improving credit scoring accuracy, speeding up response times, elevating customer experience, and ultimately increasing revenue.”
Grant said AI is also applied to chatbots and call center management, among other things.
“The use cases for artificial intelligence and machine learning are legion in the financial industry,” he said.
The Platform’s Scalable, Cost-Effective Approach Can Give Customers a Competitive Edge
Anaconda can help lenders stand out among their peers in the marketplace.
“Implementing a fast, accurate, reproducible, and cost-effective credit scoring framework is the power needed to hold a competitive edge and be a leader in the industry,” according to the website. “Anaconda Enterprise supports your organization no matter the size, easily scaling from a single user on one laptop to thousands of machines. No headaches, no IT nightmares.”
The credit scoring algorithms that can be implemented using Anaconda are predictive algorithms ready to be trained using information from past loans, according to the company. The algorithms can also be used to assess risk and predict market movement on a macro scale.
“To maintain the most accurate credit scoring, customers are continuously re-evaluated as new data is obtained about missed payments or new debt,” according to an Anaconda blog post. “Machine learning algorithms are used to update these scores as new data rolls in.”
And Grant said the platform’s invoice processing features illustrate how the tech supports speed and scalability.
“It’s a combination of technologies — image processing, optical character recognition, and natural language processing,” he said. “Every vendor’s invoice is different so it’s important to have this automated process that can interpret and categorize the data.”
Anaconda’s AI and ML technology also comes in handy when it comes to customer service, Grant said.
“If you want to improve your customer service to individual customers, you don’t want them to have to navigate that call center maze. So, the ability for them to simply describe what they are calling about in plain English and be routed to the right department is extremely valuable,” he said.
Data Science Solutions and Other Resources for a Variety of Other Industries
While Anaconda’s machine learning platform is ideal for building credit-scoring models, the company’s technology also benefits a number of industries.
“With more than 15 million users, Anaconda is the world’s most popular data science platform and the foundation of modern machine learning,” according to the website. “Anaconda Enterprise delivers data science and machine learning at speed and scale, unleashing the full potential of our customers’ data science and machine learning initiatives.”
“Extracting insights from proprietary company data so that executives can make informed decisions is key to achieving desired business outcomes,” according to the company. “To do this successfully, companies need to invest in a scalable data science infrastructure that operationalizes AI and machine learning (ML) practices.”
And of course, data scientists themselves stand to benefit greatly from Anaconda.
“Data scientists that work in silos struggle to add value for their organization. That’s why Anaconda created an integrated, end-to-end data experience,” according to the company website.
Anaconda provides the tools for data scientists to collect data from files and databases; share, collaborate on, and reproduce projects; and deploy projects into production at the click of a button.
The company’s core product for such implementations, Anaconda Enterprise, is designed for efficiency in today’s fast-moving world.
“Anaconda Enterprise combines core AI technologies, governance, and cloud-native architecture,” according to the company. “Each piece — core AI, governance, and cloud-native — are critical components to enabling organizations to automate AI at speed and scale.”
It’s understandable that you wouldn’t simply lend a stranger $100 and expect a timely repayment with interest. But for financial institutions using Anaconda, lenders can feel confident in their credit assessments and lend to borrowers who are the best fit for their business model.