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Banking and financial organisations are now under more pressure than ever before.

Increases in regulatory reporting, competition, risk management and client demands requires a data-driven approach to combat inefficiencies and provide insight into ever-increasing datasets.

It is essential that banks and financial organisations are able combine vast and varied sources to gain a clear understanding of product, fraud, risk and transactional data. By leveraging the power of analytics, data can be transformed into actionable information quicker than ever before.

CXAIR provides users with the ability to integrate data from across the organisation, breaking down the traditional barriers of analysing separated datasets. This results in organisation-wide data visibility of only the up-to-date information, available for anyone in the business to analyse and report from.

The innovative technology underpinning CXAIR is instantly familiar to anyone who has used an internet search engine, empowering business users to analyse a wide range of data on an ad hoc basis.


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CXAIR for Finance Overview

CXAIR is used within finance and banking organisations, providing the ability to quickly and easily combine disparate datasets to gain meaningful insight and improve reporting accuracy.

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Leek Building Society Case Study

Find out why Leek Building Society chose CXAIR for their reporting tool and the benefits they are now gaining from using CXAIR for finance.



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Regulatory Reporting Compliance

Automate regulatory reporting to ensure your organisation is compliant and meets all legal requirements.

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Consolidate and centralise data

CXAIR is able to integrate disparate data sets across your organisation with ease, enabling you to leverage the insight gained from your data transforming it into actionable insight.

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Improve internal reporting output

By negating the traditional overreliance on IT departments, users can report, analyse and action data faster than ever before. Users are able to quickly create complex queries to rapidly filter large datasets with the use of natural language and self-service technologies.


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