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The complete collection of all our insights, offering a comprehensive range of articles spanning foundational concepts to advanced strategies, providing guidance and inspiration for every stage of the algorithmic business journey.

Sustaining impact Frida Holzhausen Sustaining impact Frida Holzhausen

Beyond deployment: Embracing AI sustainment for lasting value

Deploying AI systems is just the beginning. To create business impact and realize value, these systems must be sustained to remain reliable, adaptive, and compliant over time. AI sustainment is a strategic approach to extend the lifecycle of AI models, ensuring they are performant, scalable, and aligned with business needs. Algorithma emphasizes a proactive methodology that continuously improves models, manages data effectively, and follows responsible AI guidelines—maximizing value and maintaining a competitive edge.

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Sustaining impact, Ensuring responsible AI Frida Holzhausen Sustaining impact, Ensuring responsible AI Frida Holzhausen

Extending Algorithma’s use-case framework: Effective data governance to mitigate AI bias

Artificial intelligence is an operational necessity in many industries, in particular financial services, driving everything from credit scoring to fraud detection. But with great power comes great responsibility: AI systems, if not managed properly, can reinforce biases and inequalities, leading to unfair lending, discriminatory insurance pricing, or biased fraud alerts. In finance, bias in AI can mean denying loans to certain groups, charging higher premiums unfairly, or disproportionately flagging transactions as suspicious—all of which have significant real-world impacts on people's lives. As AI becomes more central to finance, effective data governance is key to ensuring these systems are fair, transparent, and accountable.

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Sustaining impact Frida Holzhausen Sustaining impact Frida Holzhausen

Navigating data drift to future-proof your ML models

Companies are increasingly relying on machine learning models to make critical decisions. ML models come with a fundamental assumption: they expect the future to look like the past. In reality, the world is constantly changing, and so is the data it generates. This change, known as data drift, can silently undermine the performance of your models, leading to poor decisions, increased costs, and missed opportunities.

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Sustaining impact Jens Ekberg Sustaining impact Jens Ekberg

Managing and maintaining AI models in the long run

Building powerful AI models is just the first step. To unlock full potential and ensure responsible use, ongoing management and maintenance of these models are crucial. This involves a multifaceted approach that combines well-known best practices for application management and maintenance, with elements of data governance, data engineering, and Machine learning operations.

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