The major challenge the organization facing today is to design acquisition campaign to increase profitability and reduce cost. One of the most challenging things in designing successful marketing campaign is to balance risk and reward. Innovative risk analytics solutions of ScoreData help the organization to mitigate risk at acquisition level itself. We strongly believe that risk measures taken at acquisition level helps to build a robust and loyal portfolio and reduce the losses in long run.

Application Bad Debt Scorecard
Sometimes Consumer acquisition campaigns attract wrong kind of Consumers. To weed out the bad debt at the application level itself we advise our clients to use application level bad debt Scorecard. Early identification of potential bad debt helps the companies to take the appropriate measures to reduce the loss and to develop a quality portfolio. Our predictive analytics solutions uses only the information provided at application level to identify the potential bad debt Consumers.

Risk based pricing
Risk based pricing is common practice in industry like insurance, credit cards etc. In risk based pricing the organization calculate the "risk" involved with the applicant and apply that risk to offer different lending interest rates ,loans amount and credit limit. Our customized solution helps the banks, insurers and other money lenders to calculate the risk of each Consumer at the time of application.

Fraud Detection
Consumers who applies for loan or credit card with the sole intention of fraud behave quite differently and have different profile than non- starters and bad debt. We advise our clint to treat them differently. ScoreData applies analytics to build a risk profile associated with an identity and can create a risk-based score that predicts the likelihood of application fraud loss. Scores can be used to flag high risk applicants for manual review and more rigorous scanning. This will increase the identification of potentially fraudulent applicants at application level which in turn will help to reduce losses.

There is risk of Consumer churn to competition, pre- payment and fraud on the loan or credit limit during the lifetime of the Consumer. Our solution helps the companies to calculate the risk at regularly during the lifetime of the Consumers and take preventive measures

Retention analysis
During the lifetime of Consumer there is always some inherent risk of losing it to the competition. ScoreData specializes in building the churn and retention model for efficient Consumer Relationship Management. Our predictive solution helps the client to calculate the propensity of churn as well as the loyal Consumers. This output scores can be used to design campaign to retain the profitable Consumers.

Pre-payment Model
Prepayment is good for the borrower because it relieves him/her of the debt, but it deprives the lender of interest he/she would have received otherwise. Pre- payment model will help the client to identify these Consumers in application level itself and take proactive measures(High interest rate, Pre-Payment Penalty etc.) to counter the likely loss due to pre-payment of the Consumers.

Limit Management
In some of the products, company need to adjust the risk based profile based on the transaction of the Consumers. It is always a good idea to have a variable credit line to the Consumers based on his requirement and risk appetite of the organizations. ScoreData uses the demographics, transaction data like spending or usage and other profile data of the Consumer to build customized model to help in the Consumer limit management and hence minimizing the risk exposure of the companies and enhancing Consumer experience

Scientifically designed collection strategies have become a critical need for the consumer lending businesses. Because of the implications on both the cost and Consumer relationships, collection strategies need to deliver quality within the constraint of time and budgets. We use predictive analytics to make accurate estimates of a Consumer's propensity to repay, as well as the likely amount that the Consumer will repay. Our collections models help distinguish between self-cures and potential long term delinquent accounts only to maximize the collection from the delinquent accounts while preserving valuable Consumer relationship.

Early warning Delinquency Scorecard
The objective of this model will be to raise early alerts about the Consumers who are most likely to default or most likely to miss the payment in the next collection cycle. This will help in early identification of the risky Consumers during the life cycle of the loan so that proactive action could be taken. The unique solutions of ScoreData help the companies to proactively act before Consumer default. Our ordered scores can be used to design the collection strategies for increase collection and reduce default rates.

Normalization /Rollback Scorecard
Once the Consumers start missing its due payment it becomes increasingly difficult for to bring him back to bucket zero. Normalization model helps to identify the Consumers having greater propensity to clear their entire due amount and return to bucket zero or regular payment cycle. Our Rollback predictive model helps to segment the Consumers in collection who are more likely to make some payment and come back to lower levels.

Recovery Scorecard
Once the account is written off /charge off the account moves to recovery. ScoreData specializes in recovery model to predict the propensity of some settlement or recovery from the Consumers. We advise our client to treat early vintage and deep vintage Consumers separately. Our solution helps the banks, collection agencies and call centre working on recovery portfolios to segment portfolio based on the probability to make some payment.