How Itaú improved business incident prioritization with ML in ITSM

SES2183

Incident prioritization is challenging, and Banco Itaú takes it very seriously. Prioritizing incidents helps direct resources to critical incidents while minimizing negative impacts. Itaú reduced business incident triage time by 50% by automating the prioritization process with the following tools: CMDB data, user questions via incident forms, and machine learning. The result: 31% faster incident resolutions and MTTR.

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How Itaú reduced incidents caused by changes by 25% and problem SLA by 70%

SES1366-K23

<p>Everybody knows that data is important to help during decision-making. Itaú Unibanco, the largest bank in Latin America, reduced by 25% the volume of critical incidents caused by more than 15.000 monthly changes. To achieve this, a risk assessment feature for changes was developed, using decision tables and change policies, both being native capabilities of the ServiceNow<span style="font-size: 12.0pt;"><span style="font-family: Calibri , sans-serif;">®</span></span> platform. In addition, we are going to show how we improved incident and problem clustering, providing the best user experience by having an easier process, improving problem resolution SLA by 70%.</p>

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