intelligent system to implement financial audit and intelligent sampling regimes

TDX financial audit analytics is an intelligent, financial analytics platform that implements data-driven audit processes to achieve true efficiencies in the audit workflow. A modular approach allows the software to take advantage of both traditional statistical tools and more advanced algorithms, including artificial intelligence and machine learning, in an integrated fashion.

core benefits

Secure

Our technology uses state-of-the-art digital certificate-based security.

Privacy

Fine grained permission levels ensure only the right people access sensitive information.

Workflow Improvements

Reduce auditors time by using smart process to autofill and autocorrect critical feels.

Deep Analysis

Deep and holistic analysis of all transitions, allowing for compressive statistics to be generate.

Fraud Detection

Dynamic detection of fraud using advanced anomaly detection algorithms.

AI Integration

Unique concept of AI integrated workflow that bridges the domain of analytics and operations.

features

data fusion

Ingest and combine data from a plethora of sources provides for holistic analysis in a single platform. Sources include purchase orders, invoices, payroll, companies house, other third parties (e.g. benchmarking) and many more.

disclosure reconciliation

Automating key tasks within the audit workflow will enable auditors to conduct more in-depth investigations and have more confidence in the representatives of their sample scheme. The assurances module focuses on creating efficiencies through the automation of tasks such as general ledger-trial balance reconciliation, and account disclosures.

trend analysis

Integrating AI and machine learning into the audit process, trend analysis can be used to supplement existing workflows, providing insights and generating efficiency while conforming to existing standards. Utilising an audit evidence chain, with attribution of assertions to specific AI and ML processes, allows auditors to make their own informed decision on the output from automated processes. Additionally the software will feature the ability to tune the methods used to calculate risks to allow auditors to prioritise as they see fit.

normalised, quantised, characterisation matrices

Normalised, quantised characterisation matrices provide methods of identifying transaction outliers against a context specific distribution. This methodology is useful for clustering and making comparisons between transaction families. Plots can be viewed as distribution against time or value dimension.

analytical artefacts

The ability to add arbitrary analytical artefacts to the major objects in the accounting system (e.g. ledger entries, transaction lifecycle, account codes, approvers and companies) means an entire population can be analysed and new data can be added to the original datasets. For example, we can add clusters, risk factors, anomaly percentiles, aged dates etc on the fly.

transaction life-cycle

The automatic construction of general ledger workflow provides auditors with a new dimension of analysis. Classifying and identifying related general ledger entries into complete transaction chains, it is possible to enable new analytical methods to be applied to the audit process as well as improving existing methods. This will result in increased confidence that the select sample is representative of the full set of transactions, a key benefit of applying new techniques to audit. This methodology gives the potential for a holistic analysis of “partial” transaction integrity.

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