Over the past few years, large language models (LLMs) have dominated the conversation around Artificial Intelligence.
Let the data do the talking
Over the past few years, large language models (LLMs) have dominated the conversation around Artificial Intelligence.
𝐇𝐨𝐰 𝐜𝐨𝐦𝐛𝐢𝐧𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 + 𝐚 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞 𝐚𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞𝐬 𝐢𝐦𝐩𝐚𝐜𝐭 𝐢𝐧 𝐛𝐚𝐧𝐤𝐢𝐧𝐠 𝐚𝐧𝐝 𝐟𝐢𝐧𝐭𝐞𝐜𝐡 (𝐰𝐢𝐭𝐡 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲).
AI Under Watch – The Expert Take brings together leading experts to examine the technology, risks, ethical considerations, and implications of algorithmic decision-making. In each edition, we decode what happens behind the code and explore its real-world significance for professionals, businesses, and society as a whole.
In modern organizations, every automated decision represents a customer touchpoint, a value opportunity, or a potential friction source. That’s why decisions shouldn’t be managed in isolation – they must be orchestrated as part of an integrated strategy
Many organizations fell into the “automation at any cost” trap. They implemented automatic processes without considering logic, context or adaptability. The result? Fast but inefficient or insecure systems.
A major concern when implementing AI for critical decisions is losing the ability to explain, audit and control outcomes. The good news? Today you can have fully governed AI that automates decisions without sacrificing traceability or compliance
For years predefined rules were the backbone of decision engines. They’re clear, auditable, and create a sense of control, but they have critical limits.
For decades, financial institutions have relied on rules engines to assess risk and grant credit. These systems, based on predefined logical conditions, worked in stable environments. But today’s landscape is anything but stable.
Bank marketing departments face the challenge of mass communications with decreasing engagement. This case study reveals how a European bank increased response rates by 58% using AI that analyzes digital behavior to create ultra-personalized messages by psychological profile.
In an environment of increasing regulatory pressure and tight financial margins, leading banks are adopting predictive AI models to anticipate defaults from the first day of delinquency. This detailed analysis reveals how a European systemic institution reduced its credit provisions by 30% while improving its capital ratios.
