How GenAI Helps The Banking Sector With Fraud Detection (1).pdf
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Find out about the various challenges associated with implementing FinTech AI solutions and how to get beyond them to improve business performance, growth, and success.
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The document discusses various uses of AI in banking, including:
1) Know Your Customer/Client (KYC) and fraud detection using machine learning to analyze transactions and find anomalous patterns.
2) Anomaly detection using time series analysis to detect network issues.
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This document discusses various uses of AI in banking, including:
1) Know Your Customer/Client (KYC) and fraud detection using machine learning to analyze transactions and communications.
2) Anomaly detection using time series analysis to flag suspicious transaction patterns in real-time.
3) Customer churn prediction analyzing complex customer behavior data to identify at-risk customers.
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Machine learning is useful for fraud detection in banks by examining transaction patterns and comparing them to known fraudulent activity to identify potential fraud. It uses algorithms trained on historical data to spot these patterns and predict fraudulent transactions. However, machine learning models must be constantly updated with new information as fraud patterns change over time. It can help banks prevent fraud even when unauthorized access is not attempted by flagging suspicious behavior for human review. The benefits of machine learning for fraud detection include increased speed, efficiency, and accuracy compared to traditional methods.
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Machine learning algorithms can be used to detect credit card fraud among thousands of normal transactions. This document evaluates popular supervised and unsupervised machine learning algorithms on a highly imbalanced credit card transaction dataset to detect fraud. It was found that unsupervised learning algorithms performed best by handling the skewed data and providing the best classification results for identifying fraudulent transactions.
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A Novel Framework for Credit Card.
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Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
shyampresentaaaaaaaaaaaaaaaaaaaaaaa.pptx
ghtyfvgyhuohikbjgcfgvhkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkAir pollution is the act of mixing pollutants into air which is not ideal because it decreases the quality of life of human-beings and affects the overall planet’s habitat. Air pollution occurs when dangerous particles, gases, and chemicals are released into the air. The pollutants of air can be found in vehicle exhausts, fumes from factories and power plants, and construction sites. Respiratory problems, skin diseases, irritation of the eyes are some of the major health issues caused by air pollution. To combat this, many governments have created and enforced policies to reduce air pollution, such as shutting down coal power plants or requiring car owners to switch over to electric cars. Air purifiers are being installed at points of high vehicular movement. Rain seeding is another step to clean up the air. We should also plant more trees and care for them as trees filter pollutants and absorb carbon dioxide. Air Pollution is a challenge that humankind needs to overcome to see a better tomorrow.
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Trees provide food to man and all herbivorous animals. Animals, insects, birds, and fungi make their home in the trees and make a diverse ecosystem. Trees also help in binding the soil. When trees are cut off, the most fertile top soil layer gets washed away easily in rains or floods. Trees provide us with medicinal herbs, timber, shelter too.
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In the India of my dreams, women would be safe and be able to travel freely. Additionally, it will be a place where everyone may experience freedom and equality in its truest form. It would also be a place without caste, colour, gender, creed, social or economic standing, or race prejudice. India of my dreams should be a place where poor people get empowerment, face no poverty, do not starve, and get the proper roof to live. Additionally, I think of it as a place that experiences a lot of technological growth and development. I wish our wonderful nation nothing ggggggggg
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Whenever you use your credit card, Amazon Pay, PayPal, or PayTm to make an online payment, the commerce company/seller and your bank, both utilize FinTech to make a successful transaction. With time FinTech has changed almost and every aspect of financial services, which includes investments, insurance, payments, cryptocurrencies, and much more. Fintech companies are heavily dependent on the insights offered by machine learning, artificial intelligence, and predictive analytics to function properly.
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Card payment are mostly preferred by many for transactions instead of cash. Due to its convenience, it is the most accepted payment method for offline as well as online purchases, irrespective of region or country the purchase is made. Currently, cards are used for everyday activities, such as online shopping, bill pays, subscriptions, etc. Consequently, there are more chances of fraudulent transactions. Online transactions are the prime target as it does not require real card, only card details are enough and can be stored digitally. The current system detects the fraud transaction after the transaction is completed. Proposed system in this paper, uses Hidden Markov Model (HMM), which is one of the statistical stochastic models used to model randomly changing systems. Using Hidden Markov Model, a fraud transaction can be detected during the time of transaction itself and after 3 attempts of verification card can blocked at the same time. Behavior Analysis (BA) helps to understand the spending habits of cardholder. Hidden Markov Model helps to acquire high-level fraud analysis with a low false alarm ratio.
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The document discusses how AI and machine learning can help detect, predict, and prevent fraud by analyzing large amounts of transaction data using predictive models, which can identify patterns and behaviors across different business lines to more accurately detect fraudulent activities in real time. It also highlights the challenges of fraud detection including data silos, data overload from multiple channels and fraud types, and the need for a platform to provide collaboration and a single view of insights.5 startups using machine learning and behavioral biometrics to fight fraud
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Similar to as fraudsters are becoming more comprehensive in their attacks, so too are avenues companies can guard their data. Big data analytics is crucial for fraud detection, prevention, and risk management. As per the Association of Certified Fraud Exmainers’ Reports to the Nations, organizations proactively using data monitoring can minimize their fraud losses by an average of about 54% and identify scams in half the time.
Big data analytics is alternating the patterns in which companies prevent fraud. AI, machine learning, and data mining tech stacks help counteract the hydra of fraud attempts affecting more than 3 billion identities each year.
In short, big data analytics techniques can help identify fraudulent activities and offer actionable reports used to monitor and prevent fraud- for businesses of all sizes.
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Effective fraud detection in payment systems involves using machine learning algorithms to analyze transaction data and detect patterns of fraudulent activity. It also monitors user behavior, flags anomalous transactions that deviate from normal patterns, and implements real-time monitoring. Combining techniques such as device fingerprinting, two-factor authentication, velocity checking, network analysis, and data sharing between institutions can help create robust fraud detection systems.
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Stop Fraud in Its Tracks: How Behavior Monitoring Solutions Level Up Security
Fraud is growing globally, forcing businesses to work harder on security. One way of combating fraudulent activities effectively is through deploying such robust strategies whose costs and benefits can only be balanced properly by considering the financial or reputation consequences associated with each approach. This will include the use of advanced identification verification solutions as a critical approach. Among these, behavior monitoring solutions emerge as a proactive means to intercept and thwart fraudulent attempts before they escalate. https://www.idmerit.com/blog/how-behavior-monitoring-solutions-level-up-security/
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FraudDECK includes pre-packaged business workflows for transaction surveillance across ATM & POS channels. It can be extended to facilitate surveillance of fraudulent transactions on other channels like mobile banking or payment transactions like Wire fraud or AML. For more information please visit: http://www.esq.com/transaction-surveillance/
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Data science plays a crucial role in fraud detection by utilizing predictive analytics, anomaly detection, machine learning algorithms, pattern recognition, and data visualization to effectively identify and prevent fraudulent activities.For more information Please visit the 1stepGrow website or AI and data science course
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A Novel Framework for Credit Card.
A Novel Framework for Credit Card.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
shyampresentaaaaaaaaaaaaaaaaaaaaaaa.pptx
ghtyfvgyhuohikbjgcfgvhkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkAir pollution is the act of mixing pollutants into air which is not ideal because it decreases the quality of life of human-beings and affects the overall planet’s habitat. Air pollution occurs when dangerous particles, gases, and chemicals are released into the air. The pollutants of air can be found in vehicle exhausts, fumes from factories and power plants, and construction sites. Respiratory problems, skin diseases, irritation of the eyes are some of the major health issues caused by air pollution. To combat this, many governments have created and enforced policies to reduce air pollution, such as shutting down coal power plants or requiring car owners to switch over to electric cars. Air purifiers are being installed at points of high vehicular movement. Rain seeding is another step to clean up the air. We should also plant more trees and care for them as trees filter pollutants and absorb carbon dioxide. Air Pollution is a challenge that humankind needs to overcome to see a better tomorrow.
(166 words)
Example 2: Importance of Trees
Trees are very important, valuable and necessary to our existence as they have furnished us with two important life essentials; food and oxygen. Trees intake Carbon dioxide from air and breathe out fresh oxygen. Carbon dioxide breathed in by the trees is one of the greenhouse gases. So planting more trees will clean the air and reduce the ill – effects of global warming.
Trees provide food to man and all herbivorous animals. Animals, insects, birds, and fungi make their home in the trees and make a diverse ecosystem. Trees also help in binding the soil. When trees are cut off, the most fertile top soil layer gets washed away easily in rains or floods. Trees provide us with medicinal herbs, timber, shelter too.
Hence, We should encourage planting more and more trees. It is for our own betterment and the sooner we understand this, the better it is for us.
(150 words)
Example 3: India of my Dreams
India is a country where people of all cultures and religions coexist. As Indian citizens, we are continuously looking for ways to improve our country and see a better India.
In the India of my dreams, women would be safe and be able to travel freely. Additionally, it will be a place where everyone may experience freedom and equality in its truest form. It would also be a place without caste, colour, gender, creed, social or economic standing, or race prejudice. India of my dreams should be a place where poor people get empowerment, face no poverty, do not starve, and get the proper roof to live. Additionally, I think of it as a place that experiences a lot of technological growth and development. I wish our wonderful nation nothing ggggggggg
5 Applications of Data Science in FinTech: The Tech Behind the Booming FinTec...
https://www.datatobiz.com/blog/data-science-in-fintech/
Data Science has played a significant role in transforming thefinance and banking industry by completely changing the ways in which they previously operated. Life has been made easier for the banking officials as well as the customers. FinTech: a new term coined for the innovation and technology methods aiming to transform traditional methods of finance with data science forming one of its integral components.
Whenever you use your credit card, Amazon Pay, PayPal, or PayTm to make an online payment, the commerce company/seller and your bank, both utilize FinTech to make a successful transaction. With time FinTech has changed almost and every aspect of financial services, which includes investments, insurance, payments, cryptocurrencies, and much more. Fintech companies are heavily dependent on the insights offered by machine learning, artificial intelligence, and predictive analytics to function properly.
Online Transaction Fraud Detection using Hidden Markov Model & Behavior Analysis
Card payment are mostly preferred by many for transactions instead of cash. Due to its convenience, it is the most accepted payment method for offline as well as online purchases, irrespective of region or country the purchase is made. Currently, cards are used for everyday activities, such as online shopping, bill pays, subscriptions, etc. Consequently, there are more chances of fraudulent transactions. Online transactions are the prime target as it does not require real card, only card details are enough and can be stored digitally. The current system detects the fraud transaction after the transaction is completed. Proposed system in this paper, uses Hidden Markov Model (HMM), which is one of the statistical stochastic models used to model randomly changing systems. Using Hidden Markov Model, a fraud transaction can be detected during the time of transaction itself and after 3 attempts of verification card can blocked at the same time. Behavior Analysis (BA) helps to understand the spending habits of cardholder. Hidden Markov Model helps to acquire high-level fraud analysis with a low false alarm ratio.
Credit Card Fraud Detection Client Presentation
Presenting a Proposal to get the order from the client for implementing a Fraud Detection System in a Bank
Fraud Detection and Risk Management in Finance.pptx
Fraud detection and risk management in finance are important for protecting economic stability and investor trust. Traditional approaches rely on rules and statistics but have limitations handling complex fraud schemes. AI uses machine learning to analyze large datasets in real-time, identifying intricate patterns that indicate fraud. It enables advanced data analytics, behavioral analysis, biometric authentication, network monitoring, and automates repetitive tasks. AI techniques like supervised learning, neural networks, and anomaly detection models revolutionize fraud detection and risk assessment.
New! Omni-Channel Fraud Prevention
The document introduces Guardian Analytics' Omni-Channel Fraud Prevention and Omni-Channel Visual Analytics products. The products provide a 360 degree view of customer risk across channels using behavioral analytics and machine learning. They consolidate customer activity, risk data, and fraud alerts from multiple systems. This allows financial institutions to make faster fraud decisions and gain insights into criminal patterns across payment types and channels.
Ai and machine learning help detect, predict and prevent fraud - IBM Watson ...
Ai and machine learning help detect, predict and prevent fraud - IBM Watson ...Institute of Contemporary Sciences
The document discusses how AI and machine learning can help detect, predict, and prevent fraud by analyzing large amounts of transaction data using predictive models, which can identify patterns and behaviors across different business lines to more accurately detect fraudulent activities in real time. It also highlights the challenges of fraud detection including data silos, data overload from multiple channels and fraud types, and the need for a platform to provide collaboration and a single view of insights.5 startups using machine learning and behavioral biometrics to fight fraud
This document summarizes 5 startups that are using machine learning and behavioral biometrics to fight fraud. It provides details on each startup such as their funding amounts, investors, and business models. Some key points are that behavioral biometrics can identify users through unconscious behaviors, machine learning is used to analyze vast amounts of user data to detect fraud patterns, and the featured startups provide fraud detection and prevention platforms to help protect companies from online fraud.
Big Data Analytics Fraud Detection and Risk Management in Fintech
Similar to as fraudsters are becoming more comprehensive in their attacks, so too are avenues companies can guard their data. Big data analytics is crucial for fraud detection, prevention, and risk management. As per the Association of Certified Fraud Exmainers’ Reports to the Nations, organizations proactively using data monitoring can minimize their fraud losses by an average of about 54% and identify scams in half the time.
Big data analytics is alternating the patterns in which companies prevent fraud. AI, machine learning, and data mining tech stacks help counteract the hydra of fraud attempts affecting more than 3 billion identities each year.
In short, big data analytics techniques can help identify fraudulent activities and offer actionable reports used to monitor and prevent fraud- for businesses of all sizes.
Fraud Detection techniques - Opus Technologies.pdf
Effective fraud detection in payment systems involves using machine learning algorithms to analyze transaction data and detect patterns of fraudulent activity. It also monitors user behavior, flags anomalous transactions that deviate from normal patterns, and implements real-time monitoring. Combining techniques such as device fingerprinting, two-factor authentication, velocity checking, network analysis, and data sharing between institutions can help create robust fraud detection systems.
IRJET - Online Credit Card Fraud Detection and Prevention System
The document proposes an online credit card fraud detection and prevention system using machine learning algorithms like random forest, decision trees, and others to classify transactions as normal or fraudulent. It discusses limitations in existing fraud detection systems and outlines the proposed system which will use a random forest algorithm to detect fraud during transactions and prevent fraudulent transactions from occurring. The proposed system aims to provide higher accuracy and security compared to existing fraud detection systems.
Payments Fraud Prevention: Legit Strategies For CFOs By CXO 2.0 Conference Ex...
In this presentation, you'll discover effective payment fraud prevention strategies for CFOs at the CXO 2.0 Conference. Experts will share legitimate approaches to safeguard financial transactions, mitigate risks, and ensure the security of your organization's funds. Learn how to stay ahead of evolving fraud tactics and secure your company's financial integrity.
Stop Fraud in Its Tracks: How Behavior Monitoring Solutions Level Up Security
Fraud is growing globally, forcing businesses to work harder on security. One way of combating fraudulent activities effectively is through deploying such robust strategies whose costs and benefits can only be balanced properly by considering the financial or reputation consequences associated with each approach. This will include the use of advanced identification verification solutions as a critical approach. Among these, behavior monitoring solutions emerge as a proactive means to intercept and thwart fraudulent attempts before they escalate. https://www.idmerit.com/blog/how-behavior-monitoring-solutions-level-up-security/
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Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Essentials of Automations: Exploring Attributes & Automation Parameters
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
ScyllaDB Tablets: Rethinking Replication
ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
The Microsoft 365 Migration Tutorial For Beginner.pptx
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Apps Break Data
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Pitangent Analytics & Technology Solutions Pvt. Ltd
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Session 1 - Intro to Robotic Process Automation.pdf
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
"Scaling RAG Applications to serve millions of users", Kevin Goedecke
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Dmitrii Kamaev, PhD
Senior Product Owner - QIAGEN
"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
From Natural Language to Structured Solr Queries using LLMs
This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or “cognitive” gap) remains between the data user needs and the data producer constraints.
That is where AI – and most importantly, Natural Language Processing and Large Language Model techniques – could make a difference. This natural language, conversational engine could facilitate access and usage of the data leveraging the semantics of any data source.
The objective of the presentation is to propose a technical approach and a way forward to achieve this goal.
The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr index’s metadata.
This approach leverages the LLM’s ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
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"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
Essentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation Parameters
The Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptx
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Session 1 - Intro to Robotic Process Automation.pdf
Session 1 - Intro to Robotic Process Automation.pdf
"Scaling RAG Applications to serve millions of users", Kevin Goedecke
"Scaling RAG Applications to serve millions of users", Kevin Goedecke
Dandelion Hashtable: beyond billion requests per second on a commodity server
Dandelion Hashtable: beyond billion requests per second on a commodity server
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba
"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba
From Natural Language to Structured Solr Queries using LLMs
From Natural Language to Structured Solr Queries using LLMs
How GenAI Helps The Banking Sector With Fraud Detection (1).pdf
- 1. How GenAI Helps The Banking Sector With Fraud Detection? Presentation - 2024 www.crossml.com Visit Our Website
- 2. Anomaly Detection Behavioral Analysis GenAI algorithms can analyze vast amounts of transaction data to identify patterns and detect anomalies. Transactions that deviate from usual customer behavior or known fraud patterns can be flagged for further investigation. By leveraging GenAI, banks can analyze customer behavior over time. This analysis helps in building a profile for each customer, enabling the detection of any unusual behavior that might indicate fraudulent activity.
- 3. Real-time Monitoring Pattern Recognition GenAI systems can monitor transactions in real-time, allowing for immediate identification and response to suspicious activities. This real-time monitoring helps in preventing fraudulent transactions before they are completed. GenAI can recognize patterns associated with known fraud schemes and adapt to new patterns as they emerge. This capability enables banks to stay ahead of evolving fraud tactics.
- 4. Fraud Prediction Enhanced Security Using historical data and machine learning algorithms, GenAI can predict potential instances of fraud before they occur. By identifying high-risk transactions or customers, banks can take proactive measures to prevent fraud. Overall, the integration of GenAI in fraud detection enhances the security posture of banks, providing them with advanced tools to combat increasingly sophisticated fraudulent activities.
- 5. Looking for GenAI Solutions? Let's Connect business@crossml.com www.crossml.com +1 415 854 8690
- 6. Thank You For Your Attention www.crossml.com Visit Our Website