DLA Piper launches AI-powered solution

DLA Piper launches AI-powered solution

DLA Piper launches AI-powered solution | Insurance Business Australia

Technology

DLA Piper launches AI-powered solution

New solution designed to address bribery risks

Technology

By
Roxanne Libatique

Insurance-focused law firm DLA Piper has launched a new AI-enabled solution designed to detect bribery risks in corporations.

Called Aiscension Bribery, the solution combines DLA Piper’s legal knowledge with AI technology from Reveal. It enables investigations and compliance reviews to be run 10 times faster, more accurately, and cost-effectively than other options, it stated in a release.

“With the Australian anti-corruption landscape rapidly evolving, tools such as Aiscension Bribery may assist organisations in managing the growing compliance risk,” said Natalie Caton, Brisbane-based partner and Australian lead for white collar crime and investigations at DLA Piper. “Being found guilty of bribery and corruption pose a serious threat to organisations both in terms of fines and penalties as well as the potential damage to reputation which can be unquantifiable.”

Aiscension Bribery’s features

The AI identifies patterns and anomalies in textual data and can analyse data in over 100 languages.

Additionally, DLA Piper lawyers trained the solution’s AI to detect bribery behaviour, including giving or receiving a bribe, circumventing internal procedures, kickbacks, and facilitation payments.

Ilan Sherr, legal director at DLA Piper and executive director of Aiscension, said the new solution provides lawyers with extra time to give strategic advice and enables clients to quickly implement preventative measures.

“Being found guilty of bribery can have very serious consequences for an organisation. An infringement can involve large fines, criminal sanctions, and lasting damage to a firm’s brand and reputation. Aiscension Bribery utilises the power of AI to analyse vast amounts of data in order to unearth potential and real breaches,” Sherr said.

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