Analytical Modelling

Analytical modelling empowers organisations to achieve an enhanced understanding and visibility of their customers and provide them with valuable and incisive decision-support throughout the various stages of the customer lifecycle.

Predictive analytical models make sense of vast amounts of data, learning patterns hidden deep within the data to identify risks and opportunities in business.

Neural Technologies is a technological leader in the field of predictive analytical modelling, with over twenty years' experience of developing advanced statistical techniques to provide best in class solutions.  

The company's unique proprietary technology, deployed within varying industry sectors, is highly differentiated from the competition, leveraging clients' own customer data to achieve measureable results in the areas of customer retention and profitability, fraud and credit risk management and collections optimisation. 

Neural Technologies' analytical models are delivered as either client-hosted within an organisation's own IT environment or on the SaaS model (Software as a Service), whereby Neural Technologies hosts and operates the application for use by its clients.  The latter minimises IT expenditure and offers a managed service to fully support clients' needs as they evolve.

Customer Retention, Value & Profitability

Analytics can help organisations to acquire and retain profitable customers, and to maximise their overall lifetime value and profitability.  They enable organisations to maximise revenue by capitalising on sales opportunities in the current customer portfolio and to predict the loss of valuable customers, well in advance, to give them time to formulate effective retention strategies.  

• Identifying relevant people to receive a specific offer or product
• Identifying best and worst customers 
• Identifying cross-selling & up-selling opportunities in the current customer portfolio
• Analysing customer lifetime value
• Predicting likelihood of customer attrition

Credit Risk Management

Analytics enable organisations to make decisions about new customers, to set and adjust credit limits according to their spending and payment behaviour and to monitor them for credit risk throughout the customer lifetime.  Nt's models are used to provide individual credit limits which encourages increased spending from high value customers and helps to improve customer satisfaction. 

• Analysing for credit risk at application stage
• Determining an appropriate initial credit limit
• Managing the credit limit and credit class dynamically depending on spending/payment behaviour
• Monitoring total account exposure against calculated credit limits
• Segmentation of customer base, e.g. demographically or by behaviour

Fraud Management

Analytical models are invaluable in identifying anomalous trends and patterns within data that are likely indicators of fraudulent activity.  They provide vital protection against the possibility of fraud at the point of service application, during the crucial early stages of the customer life and throughout the customer lifetime. 

• Analysing for fraud risk at application stage
• Checking for matches against unique identifiers, such as passport, social security or national insurance number to highlight identity fraud
• Identifying suspect transactions that exceed normal limits
• Providing fraud propensity scores
• Adapting thresholds in line with changing customer behaviour to avoid false positives

Collections Management

Analytics are an ideal tool to improve the work of Collections departments.  They can help develop a focused collection strategy, making the most of limited resources by identifying which customers are likely to pay, which ones are never going to pay and which method is likely to be the most successful in recovering the outstanding debt. 

• Identifying 'auto-resolution' accounts
• Identifying 'write-off' accounts at an early stage
• Determining outstanding accounts where minimum payment is likely to be made
• Prioritising accounts to ensure maximum collections and optimised use of resources
• Identifying the most suitable strategy (or prioritising the strategies) to recover the debt