Artificial Intelligence at NetGuardians:
"From skepticism to large scale adoption towards fraud prevention"
Slides of my speech at the EPFL / EMBA Innovation Leader 2018 event.
Artificial Intelligence and Digital Banking - What about fraud prevention ?Jérôme Kehrli
Artificial intelligence for banking fraud prevention.
A presentation on how it takes its root in the digitalisation ways and how it impacts customer experience.
Future of artificial intelligence in the banking sectorusmsystems
The banking sector is becoming an active adapter of artificial intelligence — exploring and implementing this technology in new ways. The penetration of artificial intelligence in the banking sector had been unnoticed and sluggish until the advent of the era of internet banking.
Artificial Intelligence and Digital Banking - What about fraud prevention ?Jérôme Kehrli
Artificial intelligence for banking fraud prevention.
A presentation on how it takes its root in the digitalisation ways and how it impacts customer experience.
Future of artificial intelligence in the banking sectorusmsystems
The banking sector is becoming an active adapter of artificial intelligence — exploring and implementing this technology in new ways. The penetration of artificial intelligence in the banking sector had been unnoticed and sluggish until the advent of the era of internet banking.
Artificial Intelligence: a driver of innovation in the Banking Sector - The Italian case
Marco Rotoloni (Head of the research team on banking operations, ABI Lab)
KYC automation using artificial intelligence (AI)EY
Knowing Your Customer (KYC) is the process of understanding and validating the authenticity of the business’ potential clients and risk that it might impose onto the relationship. KYC solutions enable access to detailed information ensuring the credibility of clients and expediting the client onboarding. KYC solution also automate previously manual processes and reduce repetition, saving time and money for the firm.
The KYC solution streamlines the KYC process by automating the processing of customer data, sorting the data by type and storing it in a data lake. The solution reduces clutter and maintains lean operations by centralizing KYC data for any branch to query from. The solution increases operational efficiency and reduces overhead manpower cost incurred in processing consumer data manually. The time to process a client’s information is reduced from 18 minutes to 1 minute by leveraging on automation.
Find out more at www.ey.com/sg/fintechhub.
For enquiries, contact us via email at fintech@sg.ey.com.
2 billion people globally have no bank account, but 1 billion of them have a mobile phone. Markets for digital financial services are expanding worldwide.
We are all consumers of financial services more or less. We have bank accounts, possibly life insurance, some of us have credit cards, some of us have fixed deposits, some of us may be doing share trading and investment, some of us are borrowers of loans. These are all financial services. Financial Technology or FinTech is a way of delivering or improving the delivery of financial services using technology and innovation.
The use of smartphones and internet to improve the services in banking, investing, lending and borrowing etc are examples of technologies aiming to make financial services more accessible to the people. The use of Artificial intelligence, Machine learning, Blockchain, Cryptocurrency etc are redefining the way we are used to receiving financial services. FinTech is an emerging industry. Startups, established financial institutions as well as technology companies are disrupting this space to replace or enhance the usage of currently existing financial services.
In this video we will restrict ourselves to the usage of AI in FinTech.
We will learn about different areas where FinTech is already serving a great deal.
We will learn about the areas where we look forward to seeing more disruptions and innovations to make financial services more secure and accessible to the general public.
Use of Articificial Intelligence and technologies in providing financial services is what fintech does. Whether it is Payment gateway, insurance, banking, lending, stock trading, taxes.
How Fintech evolved over the years in the World and Indian Economy.
Indian Fintech Companies under different categories
Common Fintech practices adopted by Fintech Companies with better flexibility, convenience and accessibile financial products and services
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Molly Alexander
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, & ML Are Transforming the Fight Against Fraud, AML & Cybersecurity -Nadeem Asghar
The journey from open banking to open finance+. The evolution of open banking based on API as of now and where it could go from here. Risks and opportunities for market participants.
In this new Accenture Finance & Risk presentation we explore machine learning as a solution to some of the most important challenges faced by the banking sector today. To learn more, read our blog on Machine Learning in Banking: https://accntu.re/2oTVJiX
Consumers are looking for more than just banking and machine learning helps banks deliver that.
Machine learning contributes to areas such as credit decisions, risk management, personalized customer experiences, fraud detection, automation and much more.
This PDF will address the following points:
1. An overview of the banking sector and its importance in the economy
2. The top 5 banks in the US benefiting from the power of machine learning
3. The areas in banking where Machine Learning is applied
BBS-248 Artificial Intelligence (AI) for Financial ServicesOzgur Karakaya
• Artificial Intelligence (AI) general info and the AI world market
• AI in financial sector: services that AI can be applied (Investing, Management, Market Research, Blockchain, Fraud Detection, AI Assistants/Bots, etc.)
• AI firms, products and the tech behind.
The Journey to Digital Transformation with Touch BankBackbase
The presentation of Andrei Kozliar, CEO of Touch Bank. In this webinar, Jouk Pleiter, CEO of Backbase, talks to two of the most innovative banks in Europe – Touch Bank and CheBanca!
Digital transformation is about fundamentally changing how banks attract, interact with and satisfy consumers, and it affects all levels of your organisation. Antonio and Andrei will share real-life examples of digital transformation in our new webinar, which will look at:
what was needed to start their digital transformation journeys
the key elements for success.
Antonio Fratta Pasini is Head of CRM and Omni-channel for CheBanca!, the retail bank of Mediobanca Group, the third largest financial services group in Italy. CheBanca! has always been at the forefront of innovation, from flagship futuristic branches to award-winning banking apps such as WOW!
Andrei Kozliar is CEO of Touch Bank, a neobank created by OTP Bank. Founded in 1949, OTP Bank is one of the largest independent financial service providers in Central and Eastern Europe, serving nine countries. Recognizing that today’s digital-savvy customers and emerging digital natives are going to be the fastest growing customer segment, OTP Bank decided to launch a new, digital- and mobile-only bank under the label Touch Bank.
Artificial Intelligence in the Financial IndustriesGerardo Salandra
As Artificial Intelligence makes its way into our lives, many financial institutions are faced with the difficult question “Should AI be embraced?”. While the eagerness to integrate AI into the financial sector has waxed and waned over the past few decades, it now appears that Fintech is ready to dive head-first into AI as a standard for handling customer transactions, financial risk assessment, industry regulatory compliance and reduced institutional costs.
There is no doubt that AI can be invaluable for the financial industry, but it comes at a price. We expect to witness both success stories and tragic failures over the course of the next few years. With any first-generation technology, there are going to be bugs to solve, and a learning curve before intimate industry familiarity with AI is obtained.
AI is not only going to revolutionize the financial industry but become the industry itself.
Only real-time fraud detection solutions can prevent Peer-to-Peer fraud. Aite Group and Guardian Analytics show you what to be concerned about and how to detect in real-time evolving attacks from fraudsters.
Artificial Intelligence: a driver of innovation in the Banking Sector - The Italian case
Marco Rotoloni (Head of the research team on banking operations, ABI Lab)
KYC automation using artificial intelligence (AI)EY
Knowing Your Customer (KYC) is the process of understanding and validating the authenticity of the business’ potential clients and risk that it might impose onto the relationship. KYC solutions enable access to detailed information ensuring the credibility of clients and expediting the client onboarding. KYC solution also automate previously manual processes and reduce repetition, saving time and money for the firm.
The KYC solution streamlines the KYC process by automating the processing of customer data, sorting the data by type and storing it in a data lake. The solution reduces clutter and maintains lean operations by centralizing KYC data for any branch to query from. The solution increases operational efficiency and reduces overhead manpower cost incurred in processing consumer data manually. The time to process a client’s information is reduced from 18 minutes to 1 minute by leveraging on automation.
Find out more at www.ey.com/sg/fintechhub.
For enquiries, contact us via email at fintech@sg.ey.com.
2 billion people globally have no bank account, but 1 billion of them have a mobile phone. Markets for digital financial services are expanding worldwide.
We are all consumers of financial services more or less. We have bank accounts, possibly life insurance, some of us have credit cards, some of us have fixed deposits, some of us may be doing share trading and investment, some of us are borrowers of loans. These are all financial services. Financial Technology or FinTech is a way of delivering or improving the delivery of financial services using technology and innovation.
The use of smartphones and internet to improve the services in banking, investing, lending and borrowing etc are examples of technologies aiming to make financial services more accessible to the people. The use of Artificial intelligence, Machine learning, Blockchain, Cryptocurrency etc are redefining the way we are used to receiving financial services. FinTech is an emerging industry. Startups, established financial institutions as well as technology companies are disrupting this space to replace or enhance the usage of currently existing financial services.
In this video we will restrict ourselves to the usage of AI in FinTech.
We will learn about different areas where FinTech is already serving a great deal.
We will learn about the areas where we look forward to seeing more disruptions and innovations to make financial services more secure and accessible to the general public.
Use of Articificial Intelligence and technologies in providing financial services is what fintech does. Whether it is Payment gateway, insurance, banking, lending, stock trading, taxes.
How Fintech evolved over the years in the World and Indian Economy.
Indian Fintech Companies under different categories
Common Fintech practices adopted by Fintech Companies with better flexibility, convenience and accessibile financial products and services
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Molly Alexander
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, & ML Are Transforming the Fight Against Fraud, AML & Cybersecurity -Nadeem Asghar
The journey from open banking to open finance+. The evolution of open banking based on API as of now and where it could go from here. Risks and opportunities for market participants.
In this new Accenture Finance & Risk presentation we explore machine learning as a solution to some of the most important challenges faced by the banking sector today. To learn more, read our blog on Machine Learning in Banking: https://accntu.re/2oTVJiX
Consumers are looking for more than just banking and machine learning helps banks deliver that.
Machine learning contributes to areas such as credit decisions, risk management, personalized customer experiences, fraud detection, automation and much more.
This PDF will address the following points:
1. An overview of the banking sector and its importance in the economy
2. The top 5 banks in the US benefiting from the power of machine learning
3. The areas in banking where Machine Learning is applied
BBS-248 Artificial Intelligence (AI) for Financial ServicesOzgur Karakaya
• Artificial Intelligence (AI) general info and the AI world market
• AI in financial sector: services that AI can be applied (Investing, Management, Market Research, Blockchain, Fraud Detection, AI Assistants/Bots, etc.)
• AI firms, products and the tech behind.
The Journey to Digital Transformation with Touch BankBackbase
The presentation of Andrei Kozliar, CEO of Touch Bank. In this webinar, Jouk Pleiter, CEO of Backbase, talks to two of the most innovative banks in Europe – Touch Bank and CheBanca!
Digital transformation is about fundamentally changing how banks attract, interact with and satisfy consumers, and it affects all levels of your organisation. Antonio and Andrei will share real-life examples of digital transformation in our new webinar, which will look at:
what was needed to start their digital transformation journeys
the key elements for success.
Antonio Fratta Pasini is Head of CRM and Omni-channel for CheBanca!, the retail bank of Mediobanca Group, the third largest financial services group in Italy. CheBanca! has always been at the forefront of innovation, from flagship futuristic branches to award-winning banking apps such as WOW!
Andrei Kozliar is CEO of Touch Bank, a neobank created by OTP Bank. Founded in 1949, OTP Bank is one of the largest independent financial service providers in Central and Eastern Europe, serving nine countries. Recognizing that today’s digital-savvy customers and emerging digital natives are going to be the fastest growing customer segment, OTP Bank decided to launch a new, digital- and mobile-only bank under the label Touch Bank.
Artificial Intelligence in the Financial IndustriesGerardo Salandra
As Artificial Intelligence makes its way into our lives, many financial institutions are faced with the difficult question “Should AI be embraced?”. While the eagerness to integrate AI into the financial sector has waxed and waned over the past few decades, it now appears that Fintech is ready to dive head-first into AI as a standard for handling customer transactions, financial risk assessment, industry regulatory compliance and reduced institutional costs.
There is no doubt that AI can be invaluable for the financial industry, but it comes at a price. We expect to witness both success stories and tragic failures over the course of the next few years. With any first-generation technology, there are going to be bugs to solve, and a learning curve before intimate industry familiarity with AI is obtained.
AI is not only going to revolutionize the financial industry but become the industry itself.
Only real-time fraud detection solutions can prevent Peer-to-Peer fraud. Aite Group and Guardian Analytics show you what to be concerned about and how to detect in real-time evolving attacks from fraudsters.
Detecting Fraud and AML Violations In Real-Time for Banking, Telecom and eCom...TigerGraph
FULL WEBINAR: https://info.tigergraph.com/graph-gurus-3
This presentation is an overview of how to minimize fraud with TigerGraph. TigeGraph:
- Enables faster detection of fraud using deep link analytics.
- Modernizes your AML process with case studies across multiple industries.
- Helps you get fewer false positives in your fraud detection workflow.
TigerGraph is addressing these challenges for some of the largest corporations in the world including Alipay, Visa, Uber, China Mobile and SoftBank.
Leveraging Analytics to Combat Digital Fraud in Financial OrganizationsRicardo Ponce
Digitization creates major opportunities for financial services – automating operations, expanding channels, delivering engaging customer experiences. There are corresponding
challenges – unprecedented data and transaction volumes, channel control in electronic marketplaces, and preventing fraud when the fraudsters are technologically adept. To discuss the opportunities, challenges, and solutions around financial fraud in the digital age, IIA spoke with David Stewart, Director, Security Intelligence Practice-Banking at SAS Institute Inc.
Faster payments mean higher risk for fraud, especially through Business Email Compromise (BEC). Learn how to prevent fraudulent Wire transfers from identification to intervention.
with great enthusiasm Insights Success has
shortlisted The 10 Most Trusted Fraud Detection
Solution Providers, 2019, who are working round the
clock to help is clients detect fraud, faster!
This report summarizes how Innovative technologies are disrupting the financial industry and how organizations can leverage them to their advantage.
It is a must read for senior executives in banks and other financial service providers (FSPs).
Early Stage Fintech Investment Thesis (Sept 2016)Earnest Sweat
Here is an example of a personal investment thesis that I created to share with venture capital firms. In this example, I provide my personal perspective on the fintech sector. For details on how I build this thesis check out my blog (https://goo.gl/CU4Qid).
Note: Some of the confidential information has been redacted for privacy.
Analytics driving innovation and efficiency in BankingGianpaolo Zampol
Point of view around main trends and challenges to leverage Analytics in Banking industry, looking for Brazilian market landscape.
Overview on key and emerging topics: Big Data & Analytics, Fundamental Review of Trading Book (FRTB) and Risk-Adjusted Performance Management (RAPM)
Speaker: Vince Leat, Industry Consulting Executive, Teradata
Large enterprises need a partner who has done it before. Teradata has successfully implemented AI across multiple industries, proving the technology as well as producing material business outcomes. Teradata continues to channel IP from successful, field-based AI client engagements into accelerators that lead to faster time to value and reduce the risk of custom AI initiatives. Hear how Teradata helps customers build opportunities derived from AI.
Introduction to Modern Software ArchitectureJérôme Kehrli
This talk offers an introduction to software architecture with a modern perspective. We will consider a new way to identify architectural elements and walk through some examples of modern architectures, the NoSQL world, Big Data architectures and micro-services.
A proposed framework for Agile Roadmap Design and MaintenanceJérôme Kehrli
Maintaining a relevant and meaningful roadmap while adopting a state of the art Agile methodology is challenging and somewhat antonymous.
This presentation proposes a framework for designing and maintaining an Agile Roadmap.
A presentation of the search for Product-Market Fit with the principles, practices and processes that lead to it, from the Lean-Startup and Design Thinking perspective
From Product Vision to Story Map - Lean / Agile Product shapingJérôme Kehrli
A lot of Software Engineering projects fail for a lack of shared vision due to poor communication among people involved in the project.
A sound maintenance of the product backlog can only be achieved if all the people have a good understanding of what they have to do (common vision).
Roman Pichler, in a post originally written in Jul 16 2012, has proposed a really interesting approach: use various canvas to create and share product vision and product backlog creation and refinement.
This presentation is a drive through these various boards and canvas that should be designed in prior to any product development: the Product Vision, the Lean Canvas, The Product Definition and the Story Map.
Introduction to NetGuardians' Big Data Software StackJérôme Kehrli
NetGuardians is executing it's Big Data Analytics Platform on three key Big Data components underneath: ElasticSearch, Apache Mesos and Apache Spark. This is a presentation of the behaviour of this software stack.
Periodic Table of Agile Principles and PracticesJérôme Kehrli
Recently I fell by chance on the Periodic Table of the Elements... Long time no see... Remembering my physics lessons in University, I always loved that table. I remembered spending hours understanding the layout and admiring the beauty of its natural simplicity.
So I had the idea of trying the same layout, not the same approach since both are not comparable, really only the same layout for Agile Principles and Practices.
The result is in this presentation: The Periodic Table of Agile Principles and Practices:
Agility and planning : tools and processesJérôme Kehrli
In this presentation, I intend to present the fundamentals, the roles, the processes, the rituals and the values that I believe a team would need to embrace to achieve success down the line in Agile Software Development Management - Product Management, Team Management and Project Management - with the ultimate goal of making planning and forecasting as simple and efficient as it can be.
Bytecode manipulation with Javassist for fun and profitJérôme Kehrli
Java bytecode is the form of instructions that the JVM executes.
A Java programmer, normally, does not need to be aware of how Java bytecode works.
Understanding the bytecode, however, is essential to the areas of tooling and program analysis, where the applications can modify the bytecode to adjust the behavior according to the application's domain. Profilers, mocking tools, AOP, ORM frameworks, IoC Containers, boilerplate code generators, etc. require to understand Java bytecode thoroughly and come up with means of manipulating it at runtime.
Each and every of these advanced features of what is nowadays standard approaches when programming with Java require a sound understanding of the Java bytecode, not to mention completely new languages running on the JVM such as Scala or Clojure.
Bytecode manipulation is not easy though ... except with Javassist.
Of all the libraries and tools providing advanced bytecode manipulation features, Javassist is the easiest to use and the quickest to master. It takes a few minutes to every initiated Java developer to understand and be able to use Javassist efficiently. And mastering bytecode manipulation, opens a whole new world of approaches and possibilities.
DevOps is a methodology capturing the practices adopted from the very start by the web giants who had a unique opportunity as well as a strong requirement to invent new ways of working due to the very nature of their business: the need to evolve their systems at an unprecedented pace as well as extend them and their business sometimes on a daily basis.
While DevOps makes obviously a critical sense for startups, I believe that the big corporations with large and old-fashioned IT departments are actually the ones that can benefit the most from adopting these principles and practices.
Digitalization: A Challenge and An Opportunity for BanksJérôme Kehrli
Today’s banking industry era is strongly defined by a word - digital. The urgency to act is only getting severe each day. Banks using digital technologies to automate processes, improve regulatory compliance, and transform the customer experience may realize a profit upside of 40% or more, while laggards that resist digital innovation will be punished by customers, financial markets, regulators, and may see up to 35% of net profit eroded, according to a McKinsey analysis.
The vital question to answer is, do we get digitalization right? Why is it getting extremely urgent to digitize?
Some years ago, Eric Ries, Steve Blank and others initiated The Lean Startup movement. The Lean Startup is a movement, an inspiration, a set of principles and practices that any entrepreneur initiating a startup would be well advised to follow.
Projecting myself into it, I think that if I had read Ries' book before, or even better Blank's book, I would maybe own my own company today, around AirXCell or another product, instead of being disgusted and honestly not considering it for the near future.
In addition to giving a pretty important set of principles when it comes to creating and running a startup, The Lean Startup also implies an extended set of Engineering practices, especially software engineering practices.
Smart Contracts are a central component to next-generation blockchain platforms. Blockchain technology is much broader than just bitcoin. The sustained levels of robust security achieved by public cryptocurrencies have demonstrated to the world that this new wave of blockchain technologies can provide efficiencies and intangible technological benefits very similar to what the internet has done.
Blockchains are a very powerful technology, capable of going much further than only "simple" financial transaction; a technology capable of performing complex operations, capable of understanding much more than just how many bitcoins one currently has in his digital wallet.
This is where the idea of Smart Contracts come in. Smart Contracts are in the process of becoming a cornerstone for enterprise blockchain applications and will likely become one of the pillars of blockchain technology.
In this presentation, we will explore what a smart contract is, how it works, and how it is being used.
The Blockchain - The Technology behind Bitcoin Jérôme Kehrli
The blockchain and blockchain related topics are becoming increasingly discussed and studied nowadays. There is not one single day where I don't hear about it, that being on linkedin or elsewhere.
I interested myself deeply in the blockchain topic recently and this is the first article of a coming whole serie around the blockchain.
This presentation is an introduction to the blockchain, presents what it is in the light of its initial deployment in the Bitcoin project as well as all technical details and architecture concerns behind it.
We won't focus here on business applications aside from what is required to present the blockchain purpose, more concrete business applications and evolutions will be the topic of another presentation I'll post in a few weeks
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
12. Rule-based
systems are
beaten !
Every bank
customer / user is
different
Hundreds of thousands
of rules would be
required to reflect
everyone’s situation
Financial
Impacts
Reputation
Damage
14. Lacking a global
view of
activities at the
bank scale
Some
transactions are
always unusual
on a per
customer
basis
Financial
Gains
Reputation
Operational
Efficiency
Drastic reduction
of fraud cases
passing
through
Number of cases
to be
investigated
reduced to
1/3
Number of
re-validation
asked to
customers
reduced to
1/4
Average time
required to
investigate a case
reduced by
80%
16. Additional
reduction of
cases to be
investigated
(false positives)
Groups and
their profiles
form an
invaluable
information
source
Additional
Operational
Efficiency
Additional
Financial
Gains
Analyzing
non-transactional
activity requires
different analysis
techniques
Analysis of
weak signals
related to
behavioural
changes
In this article, I intent to present my company – NetGuardians’ approach when it comes to deploying Artificial Intelligence techniques towards better fraud detection and prevention.
This article is inspired from various presentations I gave on the topic (TODO link) that synthetize our experience at NetGuardians in regards to how these technologies were initially triggering a log of skepticism and condescendence and how it turns our that they are now not optional anymore to efficiently prevent fraud in financial institutions.
Qq mots
NG / 3.5 years / CTO
18 years in the software engineering business, not tired yet
technology, digitalization concerns, Artificial Intelligence and Data Analytics
Before getting to the heart of the topic, I would want to say a few words about NetGuardians, as a way to justify my legitimacy on the topic I am presenting today.
NetGuardians is a swiss software editor based in Yverdon and founded by two former students of the Engineering School in Yverdon. NetGuardians develops a Big Data Analytics platform deployed in banking institution most essentially for one big key concern : fraud prevention and detection where fraud is considered its broad way, meaning both internal fraud (employees diverting funds from their employing bank) and external fraud (Credit Card Theft, Ebanking session hijacking, etc.)
The NetGuardians Analytics platform works by correlating massive amount of data from various data sources and uses Machine Learning algorithms to learn in different ways about customer or employees habits and behaviour in order to be able to detect anomalies in this behaviour.
The Company has been founded in 2008 but really started developing in 2012 after a pretty long incubation period. It counts today 60 employees and around 50 customers all around the world.
For 3 years, the company double its incomes and signs a dozen of new customers every year. We hope we’ll keep doing this this year.
Today, I would want to present how Artificial Intelligence Technologies appear to be indispensable when it comes to preventing fraud efficiently in banking institutions.Here financial fraud is considered at the broad scale, both internal fraud, when employees divert funds from their employer and external fraud in all its forms, from sophisticated network penetration schemes to credit card theft.
I don’t have the pretention to present an absolute or global overview. Instead, I would want to present things from the perspective of NetGuardians, from our own experience in regards to the problems encountered by our customers and the how Artificial Intelligence helped us solve them.
Before 2000, banking institutions are only poorly equipped when it comes to fight financial fraud.
For most of it, detecting fraud cases relies on manual verifications and tests performed by
Internal Control,
Internal Audit or
External Audits
And unfortunately, this implies a lot of issues:
By working with samples only, Internal control and Audit let a lot of fraud cases pass through the cracks and are found only very late or even never.
Analysis are cumbersome and most often finding fraud cases is not the first and foremost objective of the auditors.
Now of course, the most essential security rules and checks are implemented within the Operational Information System or in the form of procedures to be respected and audited.
Also, some banking institutions already have an Analytics System – or Business Intelligence - at the time and some ad’hoc reports are implemented on top of it that target fraud detection.
In these early times, neither the subprime crisis nor the south European countries debt crisis happened. Margins are important, people trust banks and all in all bankers are happy people.
Fraud cases, mostly internal, exist of course but financial institutions feel rather safe,
In the second half of the 2000’s, however, the costs linked to fraud, increasingly external, the complexity of attacks and the maturity of attackers rise.
Banking institutions react by deploying quite massively and for the first time specific analytics systems aimed at detecting banking fraud, both external and internal.
At this time, these systems are rules-engines that work by checking or searching pre-defined and well defined conditions within the data extracted from the information system.
In a way these systems can be considered as simple extensions of the security checks and rules implementing directly within the operational information system.
The solutions come most of the time from the AML – Anti Money Laundering – World, their editors having understood that banking fraud was a way to extend their sales
A very simple rule example is show at the bottom of this slide.
At this time, a first set of papers have already been published on the success, still somewhat relative in this early days, of some Machine Learning approaches implemented towards banking fraud detection.
But Machine Learning and Artificial Intelligence are considered with a lot of condescension and skepticism.
Bankers and their engineers are not willing to consider an approach whose interpretation of results is deemed fuzzy.
NetGuardians has been built at these times and the NetGuardians platform could be seen as a gigantic rule engine,.
Unfortunately, the reality of fraud and financial cybercrime evolved fast and dramatically.
Let me give you two examples
In February 2016, a group that we deem around 20 persons, composed by financial experts, software engineers and hackers have attacked the information system of the Bangladesh Central Bank.
They manage to compromise the bank internal gateway to the SWIFT Network. The SWIFT network is the international banking messaging network used by banks to communicate and transfer money through electronic wire.
The pirates used the SWIFT network to withdraw money from the Bangladesh Central bank VOSTRO account by the US Federal Reserve.
They manage to transfer 81 millions USD to the Philippines and used the Philippino casinos to launder the stolen funds.
As a sidenote, the fact that they have stolen “only” 81 million USD is an amazing luck for the bank, or rather an amazing bad luck for the cybercriminals.
An Anti-Money laundering system – rule-based - deployed in the US federal Reserve blocked the 6th transaction because the beneficiary name contained the word “Jupiter”. Jupiter was on a sanction screening list in the US because a cargo ship navigating under Iranian flag is called “Jupiter” something. The 6th transaction being blocked, all the further ones, around thirty, have been blocked as well.
But 5 transactions pass through before the 6th has been blocked by the Fed and went further through the correspondent banking network
Another transaction has been blocked by the Deutsche Bank, a routing bank, because of a typo “ Shilka Fandation” instead of “Shilka Fundation”
So only 4 transactions our of 35 successfully arrived to the Philippines and as such the total loss have been reduces from 951 million USD initially intended to “only” 81 millions USD
As a fun note, a few week after the heist, all the responsibles of the financial institutions involved, the US Fed Reserve, the Bangladesh Central Bank, even the finance minister of the Philippines were all convinced that the money – or at least a significant part of it – would be recovered and that the cybercriminals would be caught.
2 years after, today, we know that we will never recover these funds.
The attacker are safe, untraceable and will never be found
We believe that this is a group of about 20 persons who worked on the heist preparation for about 18 months. 81 million USD is a pretty number.
Now you think
But this is Bengladesh … right ?
Here we are in Europe, even better, here we are in Switzerland … right ?
And in Switzerland we don’t really feel concerned by the numerous security holes in the Bangladesh Central Bank Information System
So let me give you another example…
The Retefe worm is a worm developed by a team of cybercriminals targeting specifically the ebanking platforms of small and mid size Austrian And Swiss Banking Institutions
The worm is used by the thieves to take control of the victim’s ebanking sessions and to submit fraudulent transactions to the system
This worm is 4 years old
For 4 years, fraudsters keep on updating it, modifying it and extending it to counter the anti-viruses software and the specific protections put in place by the banks.
This worm is 4 years old and nevertheless, as pointed out by the Computer security section of the federal finance department, it is still making today between 10 and 90 victims in Switzerland and Austria,
Today, in the swiss banks …
My conclusion from these examples is as follows:
Today, fraudsters and cybercriminals are professionals
The time when fraud was coming from a little hacker working in his garage or a back-office employee disappointed by his bonus, is over.
Today, attackers are professionals who have industrialized their methods
Some facts and projections to understand what reality banking institutions are facing nowadays …
In frebruary 2016, a group of cybercriminals managed to steal 81 million USD from the VOSTRO account of the Bangladesh Central Bank by the US federal Reserve
This is one of the biggest bank heist in history and the most impressive cybercrime ever
In a report called “Report to the nations”, the international association of Fraud Examiners estimated that in 2017, the total cost of fraud has been 3000 billions USD
In banking fraud, a big part of this amount is related to internal fraud, when bank employees divert funds from their employer.
In Switzerland, of course, thanks to the maturity of the banking business as well as the security checks and practices put in place in banking institutions, internal fraud is marginal, compared to external fraud.
But external fraud is a cruel reality, think of the Retefe Worm.
Finally, Cyber Security ventures estimates that by 2021 the total cost of cybercrime will reach 6000 billion USD.
La réalité à laquelle les banques sont confrontées aujourd’hui, c’est celle-ci.
The principal implication of this reality, the problem which banking institutions are confronted to nowadays is that historical systems deployed to counter fraud – rules engines – are beaten.
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Let’s assume that a banking institutions wants to define a set of rules aimed at detecting when an attacker imitates a customer to steal money from his accounts
Imagine the situation of a first customer, someone such as myself, using his ebanking account to pay his loan at the end of the month, his mortgage, his taxes, telephone bills, etc.
In my case, a big transaction withdrawing 20 k CHF from my account for a beneficiary located in Nigeria should raise an alert. It’s clearly an anomaly completely outside of my usual habits and behaviour.
Imagine now the situation of a another customer, a responsible of acquisitions for a big corporation, a frequent traveller, spending most of his time abroad and using the corporate account to pay big amounts to providers all over the world.
In the case of this second customer, a small payment benefiting to a counterparty in Switzerland would be the anomaly and should raise an alert, not a payment to an abroad counterparty.
If one wants to detect anomalies for these two different situations, one would end up implementing a completely different set of rules for the two distinct customers.
And this is impossible
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Every bank customer, and even user up to a certain level, is different.
Representing everyone’s own and private situations with rules would require to implement and manage hundreds of thousands of rules on the system, which, obviously, is impossible
Only the most common set of rules can be implemented, which means that
A lot of frauds pass through the coarse grained net
In addition, in order to catch the biggest frauds, the limits enforced by the rules have to be very low, which has the consequence of flagging a lot of cases to be analyzed (the so called false-positives) requiring an army of analysts to be reviewed and discarded
The direct consequences for our customers are
Financial impacts : frauds must be reimbursed. And these analysts spending their days discarding false positives must be paid.
Reputation impacts : a fraud case being communicated in the newspapers is a nightmare for banking institutions. Even without a large scale communication, customers impacted by fraud lose faith in their institution. I do not need to explain to you the consequences that the thousands of papers published on the Bangladesh Bank heist had on the Bangladesh central bank.
Rule-bases systems are beaten today.
Something else is required to protect efficiently Banking institutions from banking fraud.
Artificial Intelligence provides the solution to this problem
In 2016, we started at NetGuardians to integrate the first advanced algorithms, so called Machine Learning algorithms, in our systems.
We let an Artificial Intelligence analyze continuously the history of billions of transactions in the system and learn about individuals habits and behaviours.
With big data technologies, AI can analyze a very extended depth of history and build dynamic profiles for each and every individual related to a financial transactions.
Individuals are both Customer and Users (Internal Employees)
Profiling customers is required for both Internal and External Fraud.
Profiling users is required for Internal Fraud.
Big Data technologies are key to maintain these profiles up-to-date in real time by tracking each and every interaction between the user and the bank systems
In addition to a financial transaction direct characteristics such as the beneficiary, the target bank country, the amount of the transaction, its currency, etc., the machine can correlate a lot of indirect characteristics, such as where in the world was located the ATM where the user withdrawn money from, where was he connected to his ebanking session, etc.
For each and every individual a dynamic and up to date profile captures his behaviour and his habits
Then, each and every financial transaction, regardless of its type, it being a security trade order, an ATM withdrawal or an ebanking payment, is compared against the user profile and a risk score is computed.
Based on this risk score, the machine eventually decides whether the transactions is genuine or not and whether it requires further investigation by a human analyst within the bank.
The gains of this new approach, based on customer profiling done by AI, for our customers is striking.
It has been a game changing shift of paradigm.
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In the banking institutions where we can deploy this new generation approach, we almost eliminate the amount of fraud cases passing through the cracks.
And that, by still reducing to 1/3 of what it was before the number of cases flagged by the system to be reviewed by an analyst or fraud investigator (most of them being the so-called false positives)
Not only the amount of cases, but the amount of time required to investigate a case could be reduced by 80% by having the machine presenting the profile of the customer and how the individual transaction deviates from it with relevant and meaningful visualization techniques
Finally, the number of re-confirmation asked to customers could be reduce to ¼.
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Reducing the time required to investigate a case in addition to the amount of cases to be investigated as a direct financial impact: a lot less analysts are required to investigate these cases
Drastically reducing fraud cases passing through also has obvious financial impacts
Now all of this, especially reducing the number of times a re-confirmation is asked to customers has positive impacts on reputation
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Now working on a per-customer basis is sometimes still sub-optimal. Sometimes a genuine transactions is always very unusual on a per-customer basis and it is required to broaden the view of the Artificial Intelligence.
Let me give you an example
Let’s imagine that tomorrow I buy a new Audi
That would be a transaction of 60 kCHF leaving my account for a beneficiary – Amag Audi Switzerland – that I never used before.
Such a transaction, new beneficiary and huge amount is completely outside of my profile.
Based on this, the AI will decide to block the transaction, requiring a further validation from my end which will annoy me.
So how can we avoid that ?
If we look more carefully and globally at the transactions of this kind, big amounts benefiting to Amag Audi Swritzerland, among the customers with same profiles as myself, are quite usual.
The machine needs a broader view to understand that this transactions is not unusual
The machine can look at the big picture and analyze transactions at a broader scale.
Recall the Audi example. When such a transaction is very unusual for a specific customer, looking at other customers with similar conditions, habits and behaviour is required.
And here again AI comes in help.
AI can analyze behaviours and habits of customers and group together the people with same patterns. People that are the same age, same wealth level, same origins or same … will have a strong tendency to behave the same: for instance drive the same kind of car, such as an Audi, live in a flats of the same size, pay the same amount of telephone bills at the end of the month, etc.
The machine can analyze customer activities and transactions on the large scale and cluster together customers with same behaviour.
Then, these groups can be profiled just as individuals.
And finally, a transaction can be scored against the customer group profile in addition to the customer profile.
Recalling the Audi example. When scoring this specific payment against the individual profile, the transaction will be flagged as suspicious.
Scoring it against the group profile will clearly indicate that it’s a genuine transaction. People buy new Audis every day, especially in Switzerland
With this new approach, looking at the broader scale and comparing customers with each others instead of only scoring transactions in the individual context of a customer, we could improve our fraud detection system further
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The number of cases to be analyzed (false positives) could be reduced further
In addition, the groups and their profiles happen to be an invaluable source of information for other concerns such as marketing, trend analysis, etc.
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Of course reducing the number of cases to be handled by the investigation team has a direct impact on operational efficiency and induces further financial gains
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Now all of this, transaction scoring and customer clustering works amazingly but it works after the facts. The transaction has been input in the system and if we are not fast enough, depending on how we integrate within the bank information system, we can be too late, doing only fraud detection and not fraud prevention. What if we could analyze the User or customer activities even before the transaction is input one the system and detect fraud before it happens ?
What if we could interpret weak signals coming from the analysis of how the Customer interacts with the banking information system to qualify him as legitimate or potentially fraudulent ?
All of this require completely different analysis techniques.
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Let me give you a simple example of what I mean by analyzing a customer’s interaction with the banking Information system.
The interactions of a customer with the ebanking application is the simplest example I can come up with.
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Imagine the situation of a genuine user of the ebanking platform whose behaviour when inputting is payments is always the same
He logs in the ebanking platform
He looks at his account balance
He performed all his payment, from input to validation, many of them
He checks his pending orders, making sure he missed none of them
He logs out the platform
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Now if a worm hijacks the ebanking session, the worm will do none of that
The worm will likely go directly from login to payment input, validation to logout
Here I am only showing transitions but one can also consider User think time, keyboard stroke speed, etc.
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AI can analyze all this behaviour and activity tails a user or customer leaves on the banking information systems and build a model capturing this behaviour
Then, when an individual action is performed, the machine can compute the likelihood of that action to be performed by a legitimate user or an attacker based on the past activity.
And here as well, AI can build profiles of this activities and their likelihood both at individual level and group level through clustering techniques.
With this kind of analysis, by looking at all the interactions of the users or customers with the banking information systems, AI can look at all individual events and qualify these interactions as legitimate or suspicious regardless of the financial transactions being input or not on the system
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AI can detect a fraud, or the intention to commit a fraud, even before a transaction is input on the system, by analyzing the user or customer activity before inputing the transaction
In addition, by analyzing the behaviour of the customer as a whole, AI can qualify him as legitimate or suspicious and protect the information he sees if any doubt occurs, thus protecting his privacy in addition of his assets.
Finally, all this understanding of the user or customer habits and behaviour can be used to design even more advanced transaction scoring models
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This ability to detect fraud before they happen lead to further improvement of the operation efficiency and operational security of the banking institution
Protecting the customers privacy in addition to their asset is important to protect the reputation of financial institutions, This is especially important for private banking institutions
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With «AI vs AI», I wanted to illustrate the current research topics we have today at NetGuardians to improve further our algorithms.
In a few words, we see today that cybercriminals are increasingly using advanced algorithms on their end to study the banks attack surface and discover means to attack the banks and their customers.
We are in a cat and mouse game where attackers attempt to counter the security systems put in place by banking institutions, which in their turn deploy new form of algorithms and intelligence to protect them further.
In can only be looking forward to telling you more on this matter in a near future…
This brings me to the conclusion of my presentation …
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Our own experience and conclusion with AI technology and it’s concrete application in our use cases is without appeal.
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Introducing advanced algorithms, machine learning and advanced analytics techniques in our use cases has been key to help us improve the way we secure financial institutions and their customers
We could:
Reduce the fraud cases passing through and almost eliminate them
Reduce the number of cases to be analyzed and make the detection system a lot more relevant
Drastically reduce the amount of time required to investigate a case
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Today, at our customers, Artificial Intelligence monitors every single interaction between individuals, both customers or employees, and the information system, to qualify their actions as legitimate and fraudulent, in addition to analyzing with highly sophisticated models financial transactions input the system.
Today our reality is as follows: Artificial intelligence monitors human behavior on a large scale to secure banks and their customers
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But Science Fiction advances much faster than reality. Regarding artificial intelligence, the collective imagination, fed by Musk and Hollywood, is way ahead of reality
In the collective imagination, artificial intelligence today generates quite a lot of fantasies.
So let's agree on something if you do not mind.
If one calls weak artificial intelligence, an intelligence able to solve a problem in a strict context, to optimize a solution or a mathematical function, or to look for an answer to a question in a strict context, one calls a strong artificial intelligence an intelligence able to argue , to contextualize or to show sensitivity or initiative.
If progress in weak artificial intelligence is today very fast and very impressive, we do not have the slightest trace of a proof that would allow us to believe one day in the emergence of a strong artificial intelligence.
Strong artificial intelligence is science fiction.
The problem is that approach names like neural network are generating a lot of fantasy in the public imagination who take this name literally.
With neural networks, the public imagines a digital brain, whereas the reality is that of "matrices of convolutions", intensive iterative calculations carried out on gigantic numerical matrices. On the other hand, powerful technologies with less evocative names (genetic programming, random forests or "boosted gradient") raise less fantasies.
Today, these artificial intelligence techniques give the most impressive results when they help the human and not when they supplant it.
Chess is one of the first areas in which computers started beating humans.
The examples of algorithms that manage to defeat the great masters of chess in a not systematic but regular way are legion.
But these are the so-called "centaurs", sometimes amateur players, but helped by artificial intelligence, half-human, half-machines - who now win all the "freestyle" games.
I would like to mention a second example with a test performed last year
Melanoma specialists have been asked to identify cancerous lesions based on photos of skin lesions
These experts had a precision, a success rate of the order of 95%
An AI based on neural network deployed towards the same objective reached an impressive 93% accuracy, failing to beat the experts.
But a set of interns, rather students that actual doctors, accompanied and helped by an artificial intelligence have arrived at 97% accuracy, beating both Artificial Intelligence alone and experts
Today, the most impressive results of these technologies come from what is called Augmented Intelligence, when Artificial Intelligence intervenes in support of the human decision process and not to replace it.
And enhanced intelligence is exactly what we do at NetGuardians by providing bankers with the means to prevent fraud cases much more effectively.
Straigtfoward …
2 aspects I’d like to illustrate :
The ability to run these analyzes in real time. Be able to analyze the activity of bank customers and users in real time and is at the root of the difference between preventing fraud and detecting fraud. It must be possible to work very low processing times to characterize a transaction before it is placed on the market
The user experience. The deployed algorithms can be as intelligent as one can imagine, if one is not able to provide investigators and analysts with clear, concise and precise information, allowing them to understand the context of the transaction and the reasons for it. systems to block it, all this does not work. Users reject the solution. Providing analysts with extremely intuitive and visual means to understand machine decisions is essential.