Introduction to quantexa CDI syneo platform



Big data service providers use entity analysis, relationship analysis and artificial intelligence technology to help customers process data and prevent financial crimes

Enterprise overview

  • It was established in 2016 and currently has 500 employees
  • The service feature is scene decision intelligence (CDI)
  • The landing scenarios are mainly anti money laundering, anti financial fraud monitoring, data management and risk control of financial institutions
  • Problems to be solved: regulatory compliance, improve warning accuracy, reduce costs and improve industry competitiveness
  • The main customers are banks, insurance, Payment institutions, operators (CSPs) and government agencies. Known customers include HSBC, Standard Chartered Bank, dansk Bank (Denmark), Bank of New York & Mellon and ofx (Australian payment institution)

time axis


  • 2016-03
    • Founded, 15 people(6 financial crime experts). Work for anti financial crimes for HSBC, services: AML, people traffic, solve the data problems
  • 2016-09
    • SWIFT Innotribe Chanllenge Winner


  • 2017-03 3.3m in Series A investment
  • 2017-10 Microsoft Accelerator Programme Winner
  • 2017-? Synechron became a customer


  • 2018-04 Featured in Financial Times
  • 2018-04 Named in Tech Nation Future 50
  • 2018-04 HSBC became a customer
  • 2018-07 Open US office in NY and Boston
  • 2018-08 30m in Series B investment
  • 2018-09 100 employees
  • 2018-? Danske Bank a successful pilot


  • 2019-02 Featured in The Times
  • 2019-02 Host QuanCon
  • 2019-03 Appeared on CNN(TV)
  • 2019-05 Named “Cool Vendor” by Gartner
  • 2019-07 Appeared on Sky(TV)
  • 2019-09 200 employees


  • 2020-07 64.7m in Series C funding. The round was led by Evolution Equity Partners,
  • 2020-09 Engagement with BNY Mellon


  • 2021-07 153m in Series D funding from Warburg Pincus and a growing group of blue-chip investors
  • 2021-09 BNY Mellon has completed a strategic investment in Quantexa.
  • 2021-10 Quantexa 2 release – easier deployment, simplify navigation, introducing contextual search for unstructured data


  • 2022-04 Quantexa 2.1 release, introducing Geospatial Search

#Services and Solutions

Quantexa enables customers to make better decisions from data. According to the introduction of its website, it is divided into two directions: monitoring and investigation, which may be two different descriptions of the same product

Contextual monitoring

Combine internal data and external data to build a relationship network, reduce false positives, improve speed and accuracy, and identify previously undetected risks

  • Enhance detection rates with advanced models that leverage network-based context to reduce false positives and generate more accurate alerts.
  • Generate more meaningful alerts with context for investigators, leading to faster, trusted decisions.
  • Find new, previously unknown risk from external sources to optimize future alert generation.


With the help of visualization function, it can quickly respond to alerts and information requests, create individual portraits and real-time correlation and behavior maps for each customer and counterparty, and quickly identify the risk of financial crime and fraud

  • Automate manual work, and free up experts to focus on real risk.
  • Create a true single view of each customer or counterparty, and a real-time network of relevant connections and behaviors.
  • Go deeper and wider in your data to identify financial crime and fraud risks and typologies, faster.

Details of services involved

Anti money laundering KYC & AML

KYC and AML are financial regulatory requirements in most countries

  • Transaction monitoring is used to alert abnormal account transactions
  • Key monitoring list watch list
  • Identity verification is to keep the customer’s identity and organization information and ensure the accuracy and effectiveness of the actual beneficiary information
  • Case management case management
  • Behavioral analytics
  • Risk assessment: whether the transaction involves sensitive countries or regions
  • Does the client include PEP screening who holds important public positions, is sanctioned or involves any negative news / media information
  • Suspicious activity report (SARS)
  • Investigation management
  • Compliance reporting

Fraud detection

  • Custom fraud parameters
  • Pattern recognition: for banking, for insurance industry
  • Investigation record investigator notes
  • Check fraud monitoring
  • Internal fraud monitoring
  • Access security management
  • Transaction approval: for ecommerce, for crypto

Data management master data management

  • Relationship mapping
  • Data masking
  • Process management
  • Visualization
  • Match & Merge
  • Hierarchy management
  • Data source integrations
  • Multi domain / multi model multi domain
  • Data governance
  • Metadata management

Product introduction

The carrier of the above services and solutions is quantexa syneo platform The current (2022.04) latest version is 2.1

Product details

Quantexa uses big data and artificial intelligence technology to discover potential customer contacts and behaviors to address the needs of financial crime, customer insight and data analysis

Fast data import

  • Scalable, high-performance data subscription (import) without complex ETL; Automatically judge, configure, clean, analyze and standardize the existing data and structure; Out of the box, with default entity definitions and attribute settings, and pre trained models

  • Can accept structured, unstructured and semi-structured input data; Verify the data fields and identify problems during import; Provide UI to enable users to operate and solve problems

  • Quantexa provides its customers with many analysis models. Currently available models include capital market anti money laundering (including foreign exchange, stocks and precious metals), financial intelligence agency scoring, reducing false positives, trade anti money laundering, customer portrait scoring, securities anti money laundering detection, trade financing fraud, credit card application fraud, etc

  • Quantexa also offers customized modeling and skills training services

  • Use Quantexa Fusion to model complex source data and ingest it fast with no-code, scalable, high performance data preparation and ingestion – and no complex ETL.

  • Automatically infer, configure, cleanse, parse and standardize potential linking attributes from existing data schema.

  • Get started quickly with out of the box, state-of-the-art AI-tuned models. Define entities and their attributes.

Entity resolution

  • Quantexa’s entity analysis function connects internal and external data to obtain better accuracy, even for data without unique keywords; Define and create various data assets such as person, business and address, and output them to batch and pipeline processing

  • End users can drill down into an entity to see how and why different data records are matched into the same entity Users can dynamically adjust the parsing matching logic

  • Connect internal and external data sources with unprecedented accuracy, even from poor quality data without unique match keys.

  • Create data assets for people, businesses, addresses and more, and expose them through batch and real-time data pipelines.

Network generation

Use the diagram to show the real relationships between entities, including supply chain, partners, legal hierarchy, social relations, etc; Based on dynamic entities, different scenes are parsed and different associations are generated; Mining associations between users, institutions, addresses and transactions

  • Use to generate graphs that link entities into relevant, real world networks representing supply chains, associates, legal hierarchies, social connections and more.
  • Build on dynamic entity resolution to generate different networks for different use cases.
  • Reveal the context of how people, organizations, places, and transactions relate to each other.

Contextual analysis

  • Create and maintain data relationship models using quantexa asset (which may be a data asset management module within syneo and not separately introduced externally); Entity atlas analysis tool for machine learning and AI

  • Customers can import external detection models or use their own favorite analysis environment, such as knime, R or python The modeling method promotes transparency and interpretability, and can be run in batch or real time

  • Use Quantexa Assess to empower data scientists to build and maintain their own contextual models with ease.

  • Productively engineer features for machine learning and AI with native support for entity graphs and networks to build robust features for machine learning and AI.

Machine learning algorithms and applicable scenarios supported by quantexa

Visualization and exploration

  • Investigators can search various customer and transaction data obtained by the platform

  • The interface supports thousands of users to operate at the same time and make fast and accurate cooperative decisions The interface supports visual exploration and analysis, creating labels and highlighting interested data; At the same time, API is provided for the integration of third-party systems such as CRM

  • Data privacy compliance: quantexa has the ability to restrict access to customer data to allow its customers to comply with local data privacy requirements. When investigators interact with entities and maps, they can only view the data according to the user’s permissions

  • Support thousands of users with faster, more accurate, collaborative decisioning using Quantexa’s UI to search, visualize and explore context; investigate and thematically analyze; and review analytically created flags within their context, highlighting points of interest.

  • Or, use Quantexa’s APIs for external application platforms including CRM and case management.


Data import and management


Scenario analysis and investigation


Product technology stack



  • Scala
    Quantexa syneo’s main development language
  • Python
    A language commonly used by data workers for machine learning and data processing
  • R
    Data workers often use languages with rich functions, which are often used in scientific calculation, statistics, data analysis and mapping


  • PostgreSQL
    Small and medium-sized relational data storage
  • Oracle
    Large and medium-sized relational data storage, business software
  • Hadoop/Hive
    Large scale distributed storage and processing are used for computing tasks with low timeliness requirements. It is speculated that this product is mainly used to provide storage for spark streaming
  • Elastic
    Data retrieval engine, supporting distributed clusters
  • Apache Spark, Spark Streaming
    The data processing engine supports fault-tolerant high-throughput real-time stream data processing. It can run on Hadoop or Google cloud and kubernetes. It uses memory computing and has fast speed
  • Apache Kafka
    Message queue, streaming data pipeline, used to receive and temporarily store data before spark


  • Redhat Openshift (Kubernetes)

Third party services

  • Google Cloud Storage
  • Google Cloud SQL
  • AWS
  • Azure
  • Salesforce

Interface display

Only the interface of atlas analysis can be searched for the time being




These two are the new geographic location analysis functions in version 2.1



Market driven

Regulatory requirements

for financial firms’ ability to detect money laundering continue to mount. The price of failure is hefty fines (banks worldwide have paid several billion dollars in fines for AML lapses since 2010), embarrassing headlines, and potential liability for the firm’s chief AML officer in the form of personal fines and even jail time.

Innovation demand innovation

in financial services is creating an ever-growing attack surface. Faster payments and the increasing electronification of payment flows create utility for businesses, but criminals benefit from these innovations as well.

Customers’ expectations

for a smooth and easy experience put pressure on firms to reduce lag time and friction across the customer life cycle. These expectations start at the onboarding process and extend throughout the customer journey.

Legacy technology upgrading pressure

that produces high volumes of alerts, false positives, and often false negatives compounds the challenges that banks face. Banks often have to throw bodies at the problem to keep up with alert volume. This is not only expensive but often problematic in terms of finding skilled analysts to fill these positions.

Social pressure

from citizens who feel that banks, as trusted custodians, have an ethical obligation to detect and intercede in money laundering, human trafficking, and fraud incidents

Market trends

Escalating critical attacks on banks use advanced technology

Organized crime rings, rogue nations, and terrorists are all leveraging automation and artificial intelligence in their attacks on the financial ecosystem. These sophisticated attacks, combined with the growing volume of electronic payments, make it ifficult for FIs to keep pace with the rising tide of alerts.

Regulators are encouraging FIS to use more sophisticated detection techniques

Especially in the AML arena, concern over regulatory response to the use of advanced analytics has been an inhibitor to adoption. The new openness among regulators is encouraging FIs to invest in technology that can help them extract intelligence from their customer data.

Banks are looking for operational efficiencies

While many FIs initially turned to outsourcing first- and secondlevel alert triage to less expensive offshore locations, the benefits of these strategies were short-lived, as alert volumes continue to multiply. Many banks are now focused on tackling the source of the issue—dirty source data and high levels of false-positive alerts.

Option of next generation financial crisis technology is creating competitive differentiation

Firms that use advanced technologies to vet customers’ identities and transactions differentiate themselves from their competitors, as they provide more responsive and streamlined customer interactions, improve their operational efficiency, and meet regulatory requirements.

reference resources

  1. Official site
  2. 2019-08-05 Jamie Hutton, chief technology officer at Quantexa, about building a culture of compliance within the banking industry.
  3. 2020-03-02 Ian Lees is the Head of Research and Development at Quantexa, he gave an introduction to Quantexa (our hosts) at the start of this months Scala in the City, Lightbend Edition
  4. 2020-07 Quantexa Raises $64.7M to Drive Growth in Big Data and Analytics Ecosystem
  5. 2021-03-09 Jennifer Calvery, Head of Financial Crime HSBC. How HSBC Uses Technology To Combat Crime. See how HSBC is using technology to manage its data effectively and improve financial crime detection to tackle horrific crimes, from terrorist financing and human trafficking.
  6. Follows a successful 12-month engagement with BNY Mellon using Quantexa’s platform and includes an expanded relationship focused on data fabric innovation at the bank
  7. OFX with Quantexa: OFX is an Australian foreign exchange and payments company
  8. Case of using Quantexa
  9. Dun & Bradstreet partner with Quantexa
  10. Positive, PR service provider for Quantexa

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