It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. The datasets contain transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation.
Cleo was created to improve your financial health. Already, she's helped over 3 million people improve their relationship with money through simplicity and a sense of humour. She's an interface for the 99% – an AI assistant defining a new category, one that goes beyond saving up to actually changing how we feel about our finances. Through chat, Cleo hits you with ridiculously personal insights into your spending, while suggesting personalised financial products that increase your ability to save. That means we're meeting our users where they are and building the type of relationship they expect.
Paul Edwards is helping carry the age-old business of giving loans into the modern era of AI. Edwards started his career modeling animal behavior as a Ph.D. in numerical ecology. He left his lab coat behind to lead a group of data scientists at Scotiabank, based in Toronto, exploring how machine learning can improve predictions of credit risk. The team believes machine learning can both make the bank more profitable and help more people who deserve loans get them. They aim to share later this year some of their techniques in hopes of nudging the broader industry forward. The new tools are being applied to scorecards that date back to the 1950s when calculations were made with paper and pencil.
US-based Fintech firm Harvest is reportedly planning to transform the existing credit scoring systems. Like many other Fintechs, Harvest will be using artificial intelligence (AI) and machine learning (ML) algorithms to offer clients a dynamic view of their financial profile. This should help people with creating their financial wellness plans. It will also assist them with making informed decisions throughout the lifetime of any loans they decide to take out. Harvest's PRO Index (PariFi Rating & Opportunity Index) takes into consideration many different factors along with a client's credit score so that individuals and businesses are able to make better assessments and decisions related to managing their finances. The Harvest index uses advanced AI and machine learning algorithms to create a holistic or thorough financial plan for each client based on their credit score, income level, and spending habits.
Thousands of data sources exist nowadays from which we can extract, transform and load data ranging stock prices, medical records, surveys, population census, and logged behaviors, among others. Also, there's a huge variety of fields in which we can apply these techniques and a wide range of useful applications inside each field, such as fraud detection, credit scoring and asset allocation in relation to the finance domain. But how much can I contribute with this knowledge to a company? A LOT! Just put yourself in the situation of a credit risk analyst at a bank. "Should I lend money to this client or should I reject his application? How much information should I request him or her without risking to lose the interest rate associated with the lending? Are his periodical payslips enough? Or should I also ask him credit records from other financial institutions to guarantee the repayment?".
Though banks don't create AI strategies, they are increasingly using artificial intelligence and machine learning in their day-to-day business. We frequently work with them on ideation workshops, PoC, and solution implementation. Santander Consumer Bank, for example, is running workshops and researching how to use machine learning to boost the sustainability of loan portfolios. Besides credit risk modeling, there is already an impressive range of use cases for AI in banking. It covers everything, from customer service to back-office operations.
TrackStar.ai, a company led by credit industry veterans that specializes in predictive credit technology, today announced the launch of a new proprietary, predictive API designed to help lending institutions determine consumer lending potential. By utilizing this first-of-its-kind API, lenders are able to make better decisions about qualifying current and prior loan applicants. The result is lower acquisition costs and churn, all while reducing lender's reliance on outside partnerships for leads. TrackStar's API is designed for enterprise level banking institutions and lenders to help them optimize the customer acquisition and retention process. TrackStar's predictive AI layer determines which negative credit items could be removed from a customer's credit history, allowing lenders to extend offers to customers who might normally get declined or not even considered as qualifying loan applicants.
With the ever-increasing use of complex machine learning models in critical applications within the finance domain, explaining the decisions of the model has become a necessity. With applications spanning from credit scoring to credit marketing, the impact of these models is undeniable. Among the multiple ways in which one can explain the decisions of these complicated models, local post hoc model agnostic explanations have gained massive adoption. These methods allow one to explain each prediction independent of the modelling technique that was used while training. As explanations, they either give individual feature attributions or provide sufficient rules that represent conditions for a prediction to be made. The current state of the art methods use rudimentary methods to generate synthetic data around the point to be explained. This is followed by fitting simple linear models as surrogates to obtain a local interpretation of the prediction. In this paper, we seek to significantly improve on both, the method used to generate the explanations and the nature of explanations produced. We use a Generative Adversarial Network for synthetic data generation and train a piecewise linear model in the form of Linear Model Trees to be used as the surrogate model.In addition to individual feature attributions, we also provide an accompanying context to our explanations by leveraging the structure and property of our surrogate model.
GlobalData has revealed that the number of gig workers has increased in the world's largest economies, prompting lender curiosity about expanding their access to credit. Gig workers who previously struggled in the lending ecosystem due to their temporary, freelance jobs wanting steady pay cheques could now utilise artificial intelligence (AI) to convert them into creditworthy borrowers, by using alternative data. Kiran Raj, Principal Disruptive Tech Analyst at GlobalData, noted that traditional credit scoring models, such as FICO, are inherently flawed in accessing thin credit files due to their assessment of only a handful of standard data variables. Raj acknowledged that this leads to lenders reeling under pressure to make more inclusive credit decisions in real time. "This is where fintech start-ups have come into action with their AI credit scoring models almost instantly interpreting alternative data like historical payments, digital footprint and behavioural economics," Raj said.
As the Managing Director-Technology and Head of NY/Canada Business Unit at Synechron, Ravnit is currently responsible for leading multiple global client relationships, driving business development and sales, IT Strategy and consulting, execution and delivery, P&L and program management for strategic financial clients. In addition, a vital part of his role is providing thought leadership around the impact of emerging technologies (particularly AI/Machine Learning/RPA) on the financial industry. He is a key contributor to Synechron's Accelerator programs, leveraging emerging technologies to address key business challenges in the BFSI space. Ravnit has significant techno-functional hands-on experience as well as a proven track record for execution and delivery of IT Solutions in capital markets and wealth management functions across asset classes, lines of businesses and front-/mid-/back-office functions. Prior to Synechron, Ravnit was a Senior Developer at Polaris Consulting & Services Limited.