Everyone enjoys shopping for and spending money on expensive items, but nobody enjoys paying for them right away. Everyone believes it would have been too advantageous if the payment had to be made later. These situations are typically visible at the month’s end. As its name suggests, LazyPay is an app that grants you a credit, enables you to make payments over a cycle of 15 days, and displays a 3 day due date. People are literally becoming more and more lazy, and this method of payment is definitely growing in popularity.
LazyPay is a cutting-edge payment service that was created to offer a simple payment method for our everyday purchases which is fast, convenient and reliable. Users of LazyPay have access to a special credit limit of up to INR 1,000,000. It’s your credit, so you can do whatever you want with it. You may shop online across 25k+ platforms and websites. Through the LazyPay, users have the freedom to combine all of the LazyPay transactions and make consolidated payments. It offers a wide range of products, including ‘Buy Now Pay Later’, personal loans, no-cost EMIs, LazyPay Visa prepaid cards, UPI and scan and pay. These items are all dependent on just one variable: CREDIT LIMIT.
Users are able to buy things using this concept and pay for them later. There are numerous apps that allow you to make a purchase today and pay later. These apps have gradually assimilated into the lives of today’s youth. It simplifies transactions and allows for full settlement at the end of the cycle, which is normally 15 days long. You don’t have to be concerned about transaction errors or refund processing when using these apps. Additionally, since you don’t need to log into your account each time, it is a safer payment method and users find it easy to use. Pay later websites are now a common feature of merchant websites’ payment options. In India, the market for buy-now, pay-later apps is gradually expanding. The use of smartphones and the expansion of online retailers are both expected to increase the number of users of these apps.
However, as technology develops, it also gives rise to the increase in the number of hacks and frauds over such platforms so the businesses must be extremely watchful since fraudsters become more prevalent.
A credit limit is the maximum amount of credit that a financial institution or other lender will extend to a debtor for a particular line of credit. Once the paperless KYC is completed utilising data from the Aadhar Card, PAN Card, and other fundamental information related to the user, the platform grants the users credit.
In addition to these checks, it may also compute your likelihood of repaying the loan by taking into account your credit score, gender, age, monthly income, location, prior transactions, repayment history, level of education, and other loans you have with the account. Different ML models could be constructed using this type of data, and they attempt to determine whether or not to grant a customer a credit limit.
Human Behaviour and Machine Learning
In terms of risk detection, I think machine learning algorithms establish the same behaviour as a person does. When one of our friends requests to borrow money from us, we check to see if he has previously repaid the borrowed money, how many days did he take to repay the money, how much trust I can place in him, and whether he would repay the money on time. A machine learning algorithm’s decision-making process is influenced by the same factors. A slight twist, though, is that while we know our friends personally, not all of the clients of the ML algorithm do. In order to determine whether a consumer will make payments on time or not, machine learning algorithms dig in the previous data on individuals. Based on their findings, they determine a customer’s credit limit and whether or not they will be granted credit.
Machine Learning Algorithms for Credit Risk Management
Some of the algorithms that could be used for credit risk management are as follows:
1. Bagging Algorithms: It is an ensemble technique where many decision trees are created to come up to a conclusion. The results of the decision trees are summed up together and the final result is then declared. This gives us the benefit to come up with the result which is inclusive of so many small decision trees. The various decision trees are formed by the subset of the dataset by bootstrap aggregation.
2. Boosting Algorithms: It is an ensemble technique which works in sequential manner. The complete dataset is used to come up with a prediction and the error term is then added to the next prediction with giving different weights to the error terms. The process goes on till the final result is expected with the least error. The algorithm is such designed that it heavily penalizes the errors which are larger by assigning larger weights.
3. Artificial Neural Networks: This is a mathematical representation of a biological neural network that is equipped to manage non-linear and interactive patterns between variables. It assigns weights to different features and mathematical calculations are performed on the features to come up with the final output. It works on the principle of backward propagation, i.e., it tweaks its weights according to the errors obtained in the results. Again forward propagation takes place after adjusting the weights through backward propagation and the cycle continues until best results are obtained from the model.
The credit risk default datasets include information on both credit users who would repay the credit on time and credit users who would default. Before creating a machine learning model over the dataset, it must be balanced because the users who repay loans are always greater than the users who would default on it. Techniques like over-sampling, under-sampling, SMOTE, Tomek-Links, etc. could be used to balance the data.
Customer Filtering on the Platform
The credit limit for each consumer is tailored based on transaction data, mobile data, and more variables to account for risk, ability to pay, and anticipated consumption quantity. Lazy Pay has created an infrastructure with cutting-edge anti-fraud and underwriting methods in order to scale its product. The platform attracts a carefully selected customer base of convenience seekers (as opposed to credit seekers) who are devoted and valued clients, creating an inherent framework for risk mitigation. The affinity and credit models for Lazy Pay are built on machine learning methods. They needed to widen the consumer funnel so they could spot “strange behaviour” and utilise that information to construct anti-fraud systems and keep “bad” consumers out of the system.
LazyPay activities depend on data processing. Every day, the platform gets millions of signals that must be analysed, saved, and interpreted in order to enable large-scale credit determinations. In order to predict client behaviour, the platform’s data gathering engine gathers both structured and unstructured data from its network. Numerous user touchpoints simply dump data into the event store, where it is parsed by multiple subsystems asynchronously to extract the relevant data. Using this exclusive data, LazyPay is in a unique position to allow businesses to directly and instantly give customised rewards to the appropriate customers.