Use cases

Banking | Insurance | Marketing

Credit scoring

Use non-traditional data sources in credit scoring to offer loans for thin-file customers.

One of the most significant risks that banks or lending institutions face is credit risk, which is a result of a borrower’s failure to repay a loan or meet contractual obligations. As a result, a sound risk management system and practice are essential for a healthy lending operation.

Here, Niko AutoML platform can be used to build credit risk models to predict likelihood of a loan default. Once we have an accurate model based on historical loan data, we can make predictions for better risk pricing, credit approval, and portfolio management decisions.

Training:
The target variable for this use case is whether or not the loan was default. Selecting this variable as target makes this a binary classification problem.

Niko Automated Machine Learning automates significant steps of the model building process. Niko builds machine learning models in minutes and automatically selects the best fitted model, eliminating the need for time-consuming, labor-intensive manual building and testing of numerous models to find the most accurate model for your business needs.

Outcome:
Niko produces explainable report and charts to evaluate the model performance. The business owner can decide their trade-off and choose threshold of the model.

Prediction:
In order to predict the future loan application’s default probability, you can input new customer and see the result. Niko provides predictions with explainable chart in order to turn the machine-made decision into human-interpretable rationale. It explains why a particular loan decision was made complying with the regulatory requirements.

Fraud detection:

Build fraud detection model for alerting in-action fraudulent activity preventing the worst.

Debt collection:

Focus on borrowers with higher repayment rate to make an impact on your collection.

Claim prediction:

Define the insurer’s claim risk and speed up underwriting processes to prevent from adverse selection risk.

Risk management, the core function of the insurance industry has become more complex as customer data has accumulated. The insurers are demanding the personalised risk price, thus, identifying the risk for each customer helps to improve the underwriting process and risk portfolio.
Here, Niko AutoML platform can help in predicting the customers’ claim probability and pricing. Once we have an accurate model based on historical claim and insurance data, we can make predictions for better risk pricing and risk portfolio management decisions

Training:
The target variable for this use case is whether there was claim or not. Selecting this variable as target makes this a binary classification problem.

Niko AutoML platform automates significant steps of the model building process. It builds machine learning models in minutes and automatically selects the best fitted model, eliminating the need for time-consuming, labor-intensive manual building and testing of numerous models to find the most accurate model, following, it automates other steps in the modeling process, such as data processing and partitioning.

Outcome:
Niko produces explainable report and charts to evaluate the model performance. The business owner can decide their trade-off and choose threshold of the model.

Prediction:
In order to predict the future insurers’ claim probability, you can input new data and see the result. Niko provides predictions with explainable chart in order to turn the machine-made decision into human-interpretable rationale. This information helps the underwriting department to minimize loss, adjusting pricing and improving loss ratio of the company.

Cross-sell

Find your selling opportunity to your existing customers determining good leads that may boost your business.

Churn prediction

Identify the at-risk customers who have a high probability to leave your business, and take action for your churn risk.

Customer churn is an important metric to monitor because it equates to lost revenue. If a company loses enough customers, the financial consequences can be severe. Another reason why improving customer retention and reducing churn is critical is that it is generally more expensive to find new customers than it is to keep existing ones. Therefore, knowing your customer churn rate is important.

Here, Niko AutoML platform can help in identifying at-risk customers are an important step to reducing churn.

Training:
The target variable for this use case is whether the customer has left within the last month. Selecting this variable as a target makes this a binary classification problem.

Niko AutoML platform automates significant steps of the model-building process. It builds machine learning models in minutes and automatically selects the best-fitted model, eliminating the need for time-consuming, labor-intensive manual building and testing of numerous models to find the most accurate model, following it, and automating other steps in the modeling process, such as data processing and partitioning.

Outcome:
Niko produces explainable reports and charts to evaluate the model’s performance. The business owner can decide their trade-off and choose the threshold of the model.

Prediction:
In order to predict the customer churn likelihood, you can upload the feature data and see the result. Niko AutoML platform provides predictions with an explainable chart in order to turn the machine-made decision into a human-interpretable rationale. It explains why a particular prediction was made by the Shapley chart. Here, the customer care division knows with whom to interact directly and suggest personalized solutions to retain customers in the long-run