Use Cases | Telecommunications

Challenges

• Lengthy processes
• Slow decision-making
• Infrequent client touch-points
• Siloed customer information
• One-fits-all client experience

Opportunities

• Leverage AI to predict whether each application should be approved, rejected, or manually reviewed and even manual review team (branch,  region, head office)
• Configure different levels of automation or AI assistance for different segments, sectors, products and credit levels
• Guide credit origination teams to most important factors influencing credit level definition and automation

Challenges

• Many credit decisioning systems are driven by scorecards where these scorecards had been simplistic rule-based systems in the past. In the recent years, organisations started to develop scorecards with the traditional techniques. However, developing scorecards, maintaining them based on the macroeconomic dynamics and behavioral needs are difficult and time consuming

Opportunities

• Determine lending risk and payment default rates in situations using different level of information
• Simplify the process of predicting whether the customer will default on their credit or not to 
• Minimize credit losses which will an important input for the provisions of the bank
• Grow healthy portfolio limits of the institution
• Explain the reasoning behind the scoring thanks to Machine Learning Explainability algorithms (Shap values)

Challenges

• Improper and incorrect income estimation
• Short limits for affluent clients
• High limits for low-income customers
• Lack of automation for the models and using outdated models

Opportunities

• Utilize AI algorithms to increase the power of income estimation models and use 360-view of the customers to define the final income of the customer
• Assign limits based on the income of the customer which will allow accurate and proper limits for each customer
• Automate continuos-targeted models which will ensure inflation and other macroeconomic factors will be considered in the new models

Challenges

• One-fits-all pricing for each customer
• Lack of competition in the market for pricing
• Loss of price-sensitive customers due to pricing 
• Loss of revenue for non-sensitive clients

Opportunities

• Develop pricing models with the best-in-class AI algorithms
• Differentiate pricing for each segment, product and customer 
• Increase through-the-door customer flow with the campaign prices
• Increase revenue with the right pricing

Challenges

• Lack of definition for the customers who tend to miss their payments in the near future
• Standard actions for whole customers independent of customer's risk status

Opportunities

• Determine customers with the payment stress
• Differentiate early warning actions for stressful customers
• Improve credit roll rates and portfolio quality
• Decrease operational action costs and potential future collection costs

Challenges

• Determine customers with the payment stress
• Differentiate early warning actions for stressful customers
• Improve credit roll rates and portfolio quality
• Decrease operational action costs and potential future collection costs

Opportunities

• Machine learning models to estimate forward roll rate probability
• Increased collection rates
• Decreased operational costs
• Better provisioning in the company level

Challenges

• Loss of customers without prior notice
• No action for the customers based on their leaving probability or value to the company

Opportunities

• Identify customers who tend to leave in the near future with the AI algorithms
• Take actions to retain valuable existing customers based on churn prediction models
• Maintain revenue from the existing portfolio

Challenges

• Limited number of products and limited revenue from the customers
• No proactive cross-sell or up-sell actions
• One-fits-all product campaigns

Opportunities

• Identify customers who tend to buy additional products or increase usage in their existing products
• Take proactive cross-sell or up-sell actions to get share in the market for specific products from the valuable customers
• Grow the portfolio and product coverage

Challenges

• Non-activated products after product asssignment to the customers
• Lack of usage of the products

Opportunities

• Increase in the active customer base
• Increase in revenue from the products

Challenges

• Lack of granular customer segments
• Traditional customer segmentation without analytical methods

Opportunities

• Granular customer segmentation including risk segmentation or marketing segmentation concepts utilizing advanced clustering AI algorithms
• Customized customer management or risk activities based on customer segments

Challenges

• Limited information regarding the value of the client
• Standard actions to approach each client independent of the value of the client

Opportunities

• AI models can help calculate the lifetime value of the clients
• Increased revenue
• Customized client actions for affluent clients

Challenges

• Lack of strong fraud models
• Lack of adequate number of fraud events in the data

Opportunities

• AI is ideal for anomaly detection which helps to capture unusual events in the behaviour of the customer
• Fraud data events can be increased within the portfolio sample with oversampling, undersampling or class weight features