ASSURE IQ

𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐞 𝐀𝐈: 𝐍𝐚𝐯𝐢𝐠𝐚𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐁𝐚𝐥𝐚𝐧𝐜𝐞 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐑𝐢𝐬𝐤 In our latest conversation, we dove deep into the complexities of implementing AI responsibly within organizations. Here's a distilled framework to navigate the tightrope between innovation and risk: ✅ 𝐓𝐡𝐞 𝐃𝐢𝐥𝐞𝐦𝐦𝐚: Businesses face a dual challenge with AI - the risk of being left behind by not adopting AI and the potential catastrophes of irresponsible deployment. The middle ground? A structured approach to responsible AI. ✅ 𝐓𝐡𝐞 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: We propose a multi-dimensional framework focusing on Data, Decisions/Actions, and managing Drift. This framework addresses regulatory, policy, and brand considerations at each level, ensuring AI's responsible use. ✅ 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐋𝐞𝐯𝐞𝐥𝐬: - 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: Ensuring proper use and sourcing of data, underpinned by regulatory compliance, internal policies, and brand integrity. - 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐎𝐯𝐞𝐫𝐬𝐢𝐠𝐡𝐭: Building AI systems that make decisions with clear understanding and control over their actions, considering the legal, policy, and brand impacts. - 𝐌𝐚𝐧𝐚𝐠𝐢𝐧𝐠 𝐃𝐫𝐢𝐟𝐭: Recognizing that AI models evolve and may 'drift' from their intended purpose or effectiveness. Implementing controls to detect and correct this drift is crucial for long-term AI success. 🔍 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: Success in responsible AI doesn't just lie in the technology or data science. It hinges on a comprehensive governance structure that spans legal, policy, and brand perspectives, alongside technical controls. 💡 𝐀𝐜𝐭𝐢𝐨𝐧 𝐏𝐨𝐢𝐧𝐭: Businesses must prioritize building a robust governance framework around AI, ensuring that as AI evolves, it remains an asset rather than a liability. The goal is to leverage AI's transformative power while maintaining control, compliance, and brand integrity. How is your organization tackling the challenges of responsible AI? What strategies have you found effective? https://lnkd.in/gFjcbiBf #ResponsibleAI #AIethics #DataGovernance

Banks are realizing that the tech is not the gating factor to getting value from Ai. Nor is it necessary to "change everything." It is time to pivot to real, meaningful applications of GenAI— like Risk & Compliance Companies already have the innate capabilities do this work successfully, but it will require a small twist in mindset -- rather than needing to rethink everything!

Banks are starting to find very valuable outcomes from GenAI in Risk and Compliance. We got to dig into it a bit with Raj Chakraborty and Paul Given at the recent CapTech Banking AI Summit. Imagine if you could use Generative AI to: 🔹 Monitor quality of ALL interactions with the customer across all channels (rather than just sampling) for issues such as Reg E Complaints or Disputes, including new issues emerging for the first time 🔹 Structure Complaints taxonomy dynamically and prioritize Corrective Action Plans based on real-time understanding of the active challenges customers are facing 🔹 Discover emerging issues and Complaints while automatically identifying patterns that help you address the matter before it becomes a challenge for the bank 🔹 Work through MRAs and MRIAs by giving your teams an “AI Supersuit” that enables them to process information, find root causes, and remediate the issue in a fraction of the time previously required 🔹 Increase Straight Through Processing in remediation by 3x, extracting metadata around customer interactions and injecting it into the process, making your Pegasystems Complaints Platform more effective We got into how to make all that happen. Watch the full conversation here: https://lnkd.in/gpsviNvm

Banks are starting to find very valuable outcomes from GenAI in Risk and Compliance. We got to dig into it a bit with Raj Chakraborty and Paul Given at the recent CapTech Banking AI Summit. Imagine if you could use Generative AI to: 🔹 Monitor quality of ALL interactions with the customer across all channels (rather than just sampling) for issues such as Reg E Complaints or Disputes, including new issues emerging for the first time 🔹 Structure Complaints taxonomy dynamically and prioritize Corrective Action Plans based on real-time understanding of the active challenges customers are facing 🔹 Discover emerging issues and Complaints while automatically identifying patterns that help you address the matter before it becomes a challenge for the bank 🔹 Work through MRAs and MRIAs by giving your teams an “AI Supersuit” that enables them to process information, find root causes, and remediate the issue in a fraction of the time previously required 🔹 Increase Straight Through Processing in remediation by 3x, extracting metadata around customer interactions and injecting it into the process, making your Pegasystems Complaints Platform more effective We talked about how to do each of these things.

Banks are driving responsible use of Generative AI to ensure Risk and Compliance is effectively managed at every stage. In this conversation, Raj Chakraborty and Jeff Durso discuss the main framework that is working across business and technology. https://lnkd.in/gwFkdK_X