Bridging the gaps to self-sufficiency in Quantexa deployments

Introduction

Major banks that have been using Quantexa for several years often manage multiple use cases, have established development teams, and possess strong platform knowledge. Yet many still rely on external consultants for complex implementations, tuning activities, and platform upgrades.

As regulatory requirements continue to evolve and pressure increases to maximise technology investments, banks are increasingly seeking full self-sufficiency. Achieving this requires more than technical familiarity; it demands deep internal expertise, optimised operating models, and sustainable processes that can adapt as financial crime risks and regulatory expectations change.

Based on our experience across multiple Tier 1 bank engagements, we have identified five critical gaps that banks typically need to bridge in order to achieve autonomous, efficient, and scalable Quantexa operations.

Key challenges and efficiency gaps

Risk typology implementation dependency
While teams often understand core platform functionality, many lack confidence in designing, implementing, and deploying new or updated risk typologies. This creates bottlenecks when regulatory or business requirements change and sustains reliance on external consultants for scoring logic and alert configuration.

Entity resolution (ER) performance gaps
High-quality entity resolution is fundamental to Quantexa performance. Many banks continue to rely on compounds and rules defined during early deployments. Teams often struggle to diagnose over-linking and under-linking patterns, interpret ER metrics, and optimise match rules, leading to persistent false positives and operational frustration.

Platform upgrade risk and knowledge gaps
Upgrading Quantexa involves technical complexity. Without structured methodologies for managing breaking changes, deprecations, and compatibility testing, banks face operational disruption and delayed adoption of new platform capabilities.

Architectural inefficiencies across use cases
Mature implementations frequently contain duplicated scores, fragmented pipelines, and siloed architectural decisions. Common data sources may be ingested multiple times, while overlapping risk logic increases maintenance effort and limits scalability.

Tuning capability and process immaturity
Effective tuning requires a strong understanding of scoring pipelines, data-led decision-making, and collaboration between development, tuning, and investigation teams. In many banks, tuning lacks structured feedback loops, impact measurement, and pre-deployment validation, resulting in recurring false positives and inefficient use of resources.

Our recommendations: building sustainable self-sufficiency

Across these gaps, the common issue is often a lack of deep, practical understanding of how to fully leverage Quantexa. While the platform is in place, many banks lack the internal capability and confidence to design, evolve, and operate it independently as risks and regulatory expectations change.

Our self-sufficiency enablement programmes address this through hands-on capability building aligned to each bank’s maturity and strategic objectives. Rather than applying one-size-fits-all solutions, we focus on transferring the skills, frameworks, and practical know-how teams need to take ownership of their Quantexa implementation and continuously improve it.

Our approach spans five core pillars:

Risk typology implementation
We work closely with teams to translate business and regulatory requirements into effective scoring logic and alerting rules. Through guided delivery, reusable templates, and proven design patterns, teams build the capability to design, deploy, and maintain new risk typologies without external support.

Entity resolution optimisation
Existing compounds and matching logic are reviewed to identify over-linking and under-linking issues. Teams are upskilled on ER metrics, rule tuning, and validation techniques, enabling sustained improvements in data quality while reducing false positives.

Platform upgrade enablement
A structured upgrade approach is introduced, covering deprecation analysis, compatibility testing, and deployment planning. Clear risk mitigation and rollback procedures enable teams to manage platform upgrades confidently and adopt new features with minimal disruption.

Architectural review and consolidation
We assess mature deployments across use cases to identify duplicated pipelines, overlapping scores, and inefficient design patterns. By consolidating common data sources and rationalising logic, banks achieve a more scalable, maintainable, and cost-efficient architecture.

Tuning capability and process maturity
Technical upskilling is combined with robust tuning processes, including structured feedback loops between investigators and tuning teams. Defined KPIs, impact measurement, and pre-deployment validation ensure tuning decisions are data-led, measurable, and sustainable.

Conclusion

By systematically addressing these five gaps, banks can move to achieve true self-sufficiency and achieve true platform self-sufficiency. The outcome is streamlined architecture, stronger governance, reduced false positives, and faster responses to regulatory and risk change.

More importantly, this approach enables banks to maximise the value of their Quantexa investment, building resilient and scalable capabilities that evolve alongside financial crime threats and turn self-sufficiency from a long-term ambition into an operational reality.