The data-decision gap
Pressure to automate
Joining up data, creating decision models, operationalizing them and integrating them is effort-intensive. So is providing tools to help users make decisions when the models aren’t able to.
Decision intelligence sprawl
Disparate, inflexible, proprietary decision intelligence systems leads to duplicated data and a patchwork of vendors, and means your data scientists can’t use modern tools. This complex legacy landscape holds back change.
Analytical model underperformance
Incomplete input data wrecks model performance. Complex AI techniques can make models opaque for only marginal improvement. The results can be fraud losses, regulatory issues, missed prospects and more.
Enterprise-strength decision intelligence
Quantexa’s Contextual Decision Intelligence (CDI) platform ingests internal and external data at enterprise scale. It creates context from networks – graphs of entities linked by direct and subtle connections – enabling more accurate, transparent models. These models are fast to build and operationalize, whether automating decisions, augmenting users, or anything in between.
It excels in tough financial crime use cases and provides new approaches to Master Data Management and Customer Intelligence. A single data ingest supports analytical single views and networks that dynamically adapt to each use case – an industry first.
Consolidate your decision intelligence landscape, or integrate easily with it. So you can deliver transformational value quickly, and simplify.
The Contextual Decision Intelligence platform accelerates digital transformation with:
Dynamic handling of multiple use cases to avoid sprawl, data duplication and slow batch processes.
Scalability and fine-grained security enabling enterprise-wide platform sharing.
Cloud-native or on-prem, open APIs and standards-based analytics to make integration easy.
Tightly integrated exploration and visualization capabilities to support augmented decisions.
Technology powering the platform
Unified configuration underpins on-demand, dynamic decisions and high throughput batch training. Standards-based Parquet and Spark data processing lets your data scientists seamlessly use world-class open Spark, Hadoop and cloud-native analytics ecosystems.
An advanced analytics framework brings together network context, expert knowledge and AI into accurate, transparent analytical models. These deploy consistently across API-driven dynamic decisioning, batch detection and interactive operational scenarios.
Quantexa’s modular, pre-integrated platform takes you from raw data to decisions – and is open every step of the way.
Why use the Quantexa platform
Build once, use many, ingest to create a single view with networks
Apply your data to multiple use cases—without replicating data sets.
more accurate decisions
Use context to improve decision accuracy across the organization, find new opportunities and uncover risk.
Scale to billions of records
in batch or real time
Built on proven, scalable open source technologies like Hadoop, Spark and Elastic.
Future-proofed open architecture
Integrate seamlessly into your existing IT ecosystem, with flexible deployment options: native, or containerized for private and public cloud.
Ensure data transparency
Use explainable data linking, advanced AI and decision models for regulatory compliance.
Keep your data secure
Rely on granular security levels for dynamic control, with all activity audited.
Operationalize your data in a matter of months – not years.
Use entity resolution and data volume to overcome missing or poor quality data.
The state of AI in financial services
Read about how senior business and technology managers around the world are using AI in their financial institutions: from what they’re investing in and how they’re seeing ROI, to the challenges they encounter and use cases they focus on.
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See how CDI can accelerate digital transformation
See how our platform helps your organization make millions of operational decisions more accurately by turning internal and external data into context.