Aisera vs Inscribe

What is Aisera?

Aisera is an AI DevTools platform that provides AI Agents and Assistants designed to deliver business value for various teams.

Who is Aisera for?

Aisera is primarily for marketing teams looking to utilize generative AI for personalized prospect nurturing, hyper-targeted marketing campaigns, and maximizing marketing ROI.

What can I do with Aisera?

With Aisera, you can create custom AI agents using the Agent Composer, integrate various applications with Aisera Unify, and automate complex workflows using the AI Workflow Builder.

How much does Aisera cost?

Aisera publishes a custom / contact-sales pricing model — get a quote on their site.

How is Aisera different from alternatives?

Aisera differentiates itself by offering purpose-built AI agents and assistants that focus on delivering business value, along with features like TRAPS for security and compliance.

What is explainable AI in fraud detection?

Explainable AI in fraud detection refers to systems that show the reasoning behind a decision, not just the outcome. Rather than returning a risk score alone, an explainable system surfaces the specific signals, observations, and logic that led to a conclusion. This makes decisions auditable, helps analysts learn from the system, and supports compliance documentation requirements.

Why does AI explainability matter for financial institutions?

Financial institutions make high-stakes, high-consequence decisions that must be defensible to regulators, auditors, and in some cases the applicants themselves. When AI flags a document as fraudulent or recommends rejecting an application, risk teams need to document why. A black box system that only returns a score creates a compliance gap and erodes analyst trust in the tool over time.

What is a black box AI system?

A black box AI system is one where the internal reasoning is not visible to the user. The system accepts inputs, processes them using models or rules that aren't exposed, and returns an output, typically a score or decision, without showing its work. In fraud detection, this means analysts can't verify whether a flag is accurate, can't learn from the system's findings, and can't produce documentation explaining the decision.

How is non-determinism in LLMs handled in fraud detection?

Large language models have a temperature parameter that controls how variable their outputs are. For fraud detection, this is typically set to zero, which means the system is configured for maximum consistency — given the same inputs, it is more likely to produce similar conclusions. Some variance at the infrastructure level is unavoidable with any large language model, but the effect is minimal and the reasoning remains logically stable across runs. It's also worth separating this from a related but distinct point: an LLM's ability to generalize is a feature, not a liability. A reasoning model that doesn't simply pattern-match on previously seen cases is better equipped to catch new and evolving fraud types — and that capability comes from how the model was trained to reason, not from temperature. You can have both consistency at inference time and strong generalization. The two aren't in tension.

What questions should I ask an AI fraud detection vendor about explainability?

Start with four: Is there a human in the loop, or is the system making fully automated decisions? Can it produce audit-ready documentation for every decision? Is the reasoning surfaced proactively in the workflow, or only available if you ask for it? And what happens when the system is wrong? Can analysts follow the logic to identify where it broke down? Vendors who can answer these clearly are worth a closer look.