Today, it's more important than ever (and yet more challenging than ever) to identify high risk customers who are likely to default on your loan.
We can help you separate defaulters from good customers to minimize credit risk. Our models can provide you with a more accurate estimation of Expected Loss and Expected Loss Given Default.
Our approach leverages our proprietary algorithms, other time-tested scorecard development best practices and latest developments in data science and machine learning, to develop high performing and stable scorecards.
We follow all regulatory guidelines for model development and support our clients with the documentation and follow up reports and analysis needed in the regulatory review process.
Fraud has been on the rise in the Lending Industry. Traditionally, organizations have deployed predictive models to detect fraud. This approach uses past incidents of fraud and data science/machine learning algorithms to train the model.
Our fraud analytics approach augments the traditional approach for Fraud modeling with unsupervised machine learning algorithms to identify anomalous applications. The basic premise of the approach is that, while fraudulent applications might be able to mimic some of the main characteristics of a legitimate application, if the observed metrics are expanded, anomalies might start showing up. This model acts like an Early Warning System for fraud detection, and applications flagged by this model, can then be put through an additional level of scrutiny.
Jigyasa holds regular educational webinars on various topics related to credit risk. Please click here to visit our Eventbrite page and find out more.
Businesses are always on the lookout for new prospects, but keeping existing customers happy is crucial for preventing churn and maximizing the lifetime value of each customer.
Jigyasa can identify customers who are at risk of churn and develop trigger-induced alerts to prevent them from leaving. With the help of our analytical approaches, we can segment customers and offer the best incentive to each segment, preventing churn and maximizing profits at the same time.
With multiple campaigns running at the same time, it is difficult to identify which one of these attracted the customer and/or moved them towards conversion.
Jigyasa can accurately measure the performance, effectiveness, and ROI of an organization's marketing channels with its RapidMMM methodology. Our approach helps organizations determine how to reallocate their marketing budget among different channels to maximize sales and profits.
Outbound Direct Marketing and Cold Call campaigns can be expensive especially if organizations mail or call too many prospects. For inbound marketing too, following up on all leads can be very expensive.
Jigyasa can help organizations identify the demographic and behavioral profiles of their most profitable prospects and enable them to target the ones most likely to respond to an outreach effort with the help of machine learning models. Sometimes different segments of customers respond to different types of campaigns and identifying what works can improve campaign performance and lower marketing costs.