Capabilities in Action

How we think. How we'd approach your problem.

As a growing firm, we'd rather show you how we think than pad this page with logos. Below are the methodologies we follow and example scenarios showing how we'd apply SAP and AI capability to real BFSI and enterprise problems.

Methodology

How we approach the work.

The frameworks we use to structure engagements — the same ones we'd bring to yours.

Implementation

How We Approach an S/4HANA Migration

Start with a Clean Core Readiness Assessment to score custom code and extensibility gaps. Decide Greenfield vs. Brownfield based on that score, not preference. Run RISE with SAP with a phased cutover plan, validated against business-critical processes before go-live.

AMS

How We Structure an AMS Engagement

Begin with a 2–4 week landscape assessment to baseline ticket volume, system health, and technical debt. Set tiered SLAs (L1–L3) around that baseline, then shift focus from break-fix toward proactive optimization within the first quarter.

BTP

How We Design a BTP Integration Layer

Map every system that needs to talk to SAP before writing a single integration. Prioritize Integration Suite for standard connections, reserve custom extensions for genuine gaps, and keep every build side-by-side to protect Clean Core.

Concept Scenarios

Illustrative BFSI use cases.

These are example scenarios showing how our SAP BTP and SAP Business AI capability would apply to common BFSI problems — not completed client engagements.

A note on this section: Avant Technology Solutions is a growing firm, and the scenarios below are illustrative concepts we use to demonstrate our approach — not case studies from completed client projects. We'll update this page with real client work as those engagements go live.
Concept Scenario

Core Banking to SAP Finance Integration

ProblemA bank's core banking platform and SAP Finance run as disconnected systems, forcing manual reconciliation every close cycle.
ApproachBuild a real-time integration layer on SAP BTP Integration Suite connecting core banking transactions directly into SAP Finance, with validation rules at the point of entry.
OutcomeEliminates manual reconciliation, shortens close cycles, and gives finance real-time visibility into transaction-level data.
Concept Scenario

Fraud Scoring with SAP Business AI

ProblemRule-based fraud detection generates high false-positive rates, burying real fraud alerts under manual review backlogs.
ApproachLayer SAP Business AI-driven transaction scoring on top of existing rules, using historical transaction data already in SAP to flag genuinely anomalous behavior.
OutcomeReduces false positives, prioritizes the alerts analysts actually need to review, and shortens time-to-detection.
Concept Scenario

Insurance Claims Automation

ProblemClaims intake and initial assessment are manual, slowing down processing time and creating inconsistent decisions across adjusters.
ApproachUse document intelligence to extract claim data automatically, then apply SAP Business AI to route straightforward claims for auto-approval and flag complex ones for review.
OutcomeFaster claims turnaround, more consistent decisions, and adjusters focused only on the claims that need human judgment.
Concept Scenario

Regulatory Reporting Automation

ProblemRegulatory reports are compiled manually from multiple SAP modules, creating audit risk and consuming days of finance team time each cycle.
ApproachBuild a Clean Core-compliant reporting layer on SAP Analytics Cloud, pulling directly from source modules with a full audit trail built in.
OutcomeCuts reporting time significantly, reduces manual error, and gives auditors a traceable path from report to source data.

Have a specific problem in mind?

Tell us what you're working with, and we'll walk you through how we'd approach it — the same way we've outlined above.

Discuss Your Use Case