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.
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.
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.
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.
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.
新技术的采用给银行等金融企业创造竞争优势 Adoption of next-generation financial crime 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.
2019-08-05 Jamie Hutton, chief technology officer at Quantexa, about building a culture of compliance within the banking industry. https://www.youtube.com/watch?v=X5vaAGfytA8
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 https://www.youtube.com/watch?v=f5A1R_JCvqA
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. https://www.youtube.com/watch?v=JmnI2K6OVNg
