Graph RAG represents a shift from text-centric retrieval to structure-aware understanding to enable: 1. More consistent answers 2. Better global context 3. Reduced hallucination 4. Stronger reasoning over relationships Rather than asking LLMs to guess structure from text at generation time, Graph RAG makes structure explicit upfront.

Data Detail is Crucial: To perform causal analytics like Marketing Mix Modeling, you must preserve raw, event-level data (joint distributions). Summary reports (marginals) discard the necessary context to attribute success to specific combinations of customer traits. Avoid the "Average" Trap: Aggregated data can hide the true story. Two completely different customer behaviors can look identical in summary reports, making accurate attribution impossible without the raw source

AI is redefining finance, but trust remains its currency. As AI systems make increasingly high-stakes decisions—who gets a loan, who’s flagged for AML, who receives an offer—the cost of error multiplies. The message for financial leaders is clear: A black-box AI might optimize for accuracy but can expose the bank to unseen bias, regulatory penalties, or wasted budgets. A Responsible AI framework builds resilience, customer confidence, and long-term value.

Author:  Ting-Yuan Wang, Solution Architect at Vizuro Realities of AI Implementation Over the past eight years, Vizuro has witnessed the rapid development and widespread application of AI across various industries. According to our client statistics, AI applications are most prevalent in the healthcare sector, accounting for 34%, followed by 25% in the pharmaceutical and life sciences industries, and 23% in manufacturing. Despite the enthusiasm and expectations surrounding AI