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Quantexa CDI(场景决策智能)Syneo平台介绍

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Quantexa

 

大数据服务提供商, 使用实体解析, 关系分析和人工智能技术帮助客户进行数据处理和预防金融犯罪.

 

企业概览

2016年成立, 当前规模500人
服务特色是场景决策智能CDI(contextual decision intelligence)
落地场景主要是金融机构的反洗钱反金融诈骗监控, 数据管理, 风控
解决的问题: 监管合规, 提高警告准确率, 降低成本, 提高行业竞争力
面向的主要客户是银行, 保险, 支付机构, 运营商(CSP)和政府机构, 已知客户有汇丰银行, 渣打银行, 丹斯克银行(丹麦), 纽约&梅隆银行, OFX(澳洲支付机构)

时间轴

 

2016

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

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

2018

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

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

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

2021

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

2022-04 Quantexa 2.1 release, introducing Geospatial Search

# 服务和解决方案

 

Quantexa使客户能够从数据中做出更好的决策, 根据其网站介绍, 分为监控和调查两个方向, 可能是同一个产品的两个不同侧重的说明.

 

场景监控 contextual monitoring

 

结合内部数据和外部数据构建关系网络,降低误报, 提高速度和准确率, 并识别之前未发现的风险

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.

调查 investigations

 

借助可视化功能快速响应警报和信息请求, 对每个客户和交易对手创建单独画像以及实时的关联和行为图谱, 更快识别金融犯罪和欺诈风险.

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.

涉及的服务明细

 

反洗钱 KYC & AML

 

KYC和AML是大部分国家都存在的金融业监管要求

交易监控 Transaction Monitoring, 对异常的账户交易发出预警
重点监控名单 Watch List
身份校验 Identity Verification, 保管客户的身份以及机构信息,确保实际受益人信息的准确性以及有效性
案例管理 Case Management
行为分析 Behavioral Analytics
风险评估 Risk Assessment, 交易是否涉及敏感国家或地区
客户是否包括担任重要公职的人员 PEP Screening, 受制裁或涉及任何负面新闻/媒体信息
可疑行为报告 SARs (suspicious activity report)
调查管理 Investigation Management
合规报告 Compliance Reporting

欺诈检测 Fraud Detection

自定义欺诈参数 Custom Fraud Parameters
模式识别, 银行业/保险业 Pattern Recognition: for Banking, for Insurance Industry
调查记录 Investigator Notes
支票欺诈监控 Check Fraud Monitoring
内部欺诈监控 Internal Fraud Monitoring
权限安全管理 Access Security Management
针对电商和数字货币的交易审核 Transaction Approval: for eCommerce, for Crypto

数据管理 Master Data Management

关系映射 Relationship Mapping
数据屏蔽 Data Masking
流程管理 Process Management
可视化 Visualization
匹配和合并 Match & Merge
层级管理 Hierarchy Management
数据源集成 Data Source Integrations
多领域/多模型 Multi-Domain
数据治理 Data Governance
元数据管理 Metadata Management

产品介绍

 

以上服务和解决方案的载体为 Quantexa Syneo 平台. 当前(2022.04)最新版本为2.1

 

产品明细

 

Quantexa利用大数据和人工智能技术,发现潜在的客户联系和行为,以解决金融犯罪、客户洞察和数据分析方面的需求

 

快速数据导入 Rapid data ingestion

 

可扩展, 高性能的数据订阅(导入), 不需要复杂的ETL; 对现有的数据和结构进行自动判断, 配置, 清洗, 解析和标准化; 开箱即用, 带默认的实体定义和属性设置, 带预先训练好的模型

 

可以接受结构化, 非结构化和半结构化的输入数据; 导入时验证数据字段, 识别问题; 提供UI使用户能够进行操作并解决问题

 

Quantexa 为其客户提供了许多分析模型, 目前可用的模型包括资本市场反洗钱(包括外汇、股票和贵金属), 融情报机构评分, 减少误报, 贸易反洗钱, 客户画像评分, 证券反洗钱检测, 贸易融资欺诈, 信用卡申请欺诈等

 

Quantexa 还提供定制建模和技能培训服务.

 

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.

 

实体解析 Entity Resolution

 

Quantexa的实体解析功连接内部和外部数据得到更好的准确率, 甚至对于没有唯一关键词的数据也能得到较好效果; 定义和创建人, 业务, 地址等各种数据资产并输出给批量和流水线处理

 

最终用户可以深入到一个实体中,查看不同的数据记录如何以及为什幺被匹配到同一个实体中. 用户可以动态调整解析匹配逻辑.

 

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.

 

关系图谱 Network Generation

 

使用图展示实体之间的真实关联, 这些关联包括供应链, 合作伙伴, 法律层级, 社会关系等; 基于动态实体解析为不同的场景, 并生成不同的关联; 挖掘用户, 机构, 地址和交易之间的关联

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.

关联(场景)分析 Contextual analytics

 

使用Quantexa Assess(可能是Syneo内部的一个数据资产管理模块, 外部并无单独介绍)创建和维护数据关系模型; 为机器学习和AI服务的实体图谱分析工具.

 

客户能够导入外部检测模型或使用他们自己喜欢的分析环境, 如KNIME, R或Python. 建模方法促进了透明性和可解释性,并且可以批量或实时运行.

 

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.

 

Quantexa支持的机器学习算法和适用场景

 

可视化和探查 Visualization and exploration

 

调查人员可以搜索平台获取的各种客户和交易数据

 

界面支持上千用户同时操作, 进行快速和精确的合作决策. 界面支持可视化探索和分析, 创建标签, 高亮感兴趣的数据; 同时提供API给第三方系统如CRM等进行集成

 

数据隐私合规: Quantexa具有限制对客户数据访问的能力,以允许其客户遵守当地的数据隐私要求。当调查人员与实体和图谱交互时,他们只能根据用户的权限查看数据.

 

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.

 

工作流程

 

数据导入和管理

 

 

场景分析和调查

 

 

产品技术栈

 

 

语言

Scala
Quantexa Syneo的主要开发语言
Python
数据工作者常用语言, 用于机器学习以及数据处理
R
数据工作者常用语言, 函数丰富, 常用于科学计算, 统计和数据分析, 作图

存储

PostgreSQL
中小型关系数据存储
Oracle
大中型关系数据存储, 商业软件
Hadoop/Hive
大型分布式存储和处理, 用于时效性要求不高的计算任务, 猜测在这个产品中主要用于给Spark Streaming提供存储
Elastic
数据检索引擎, 支持分布式集群
Apache Spark, Spark Streaming
数据处理引擎, 支持容错的高吞吐量实时流数据处理, 可以运行在Hadoop或Google Cloud, Kubernetes之上, 使用内存计算, 速度较快
Apache Kafka
消息队列, 流式数据管道, 用于在Spark前接收和暂存数据

容器

Redhat Openshift (Kubernetes)

第三方服务

Google Cloud Storage
Google Cloud SQL
AWS
Azure
Salesforce

界面展示

 

暂时只能搜索到图谱分析部分的界面

 

 

 

 

这两个是版本2.1中新增的地理位置分析功能

 

 

 

市场驱动

 

监管需求 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.

 

创新需求 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.

 

客户期望 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.

 

历史遗留技术升级压力 Legacy technology

 

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.

 

舆论压力 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

 

市场趋势 Trends

 

针对银行的犯罪攻击技术在不断升级 Escalating criminal 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.

 

监管机构希望金融机构升级技术协助其更好提升情报能力 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.

 

银行希望提高运营效率 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.

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