#Machine learning in banking

[mohammedhilles]

There is no meaning for life in gaza lots of displaced people and families and children there tow photos here for the school where family members stay right now and can imagine the suffering when they just try to do their routine thing .and the othere tow photos are for the area around the school no need to explain how bad it is .

Help me evacuate my family from Gaza somehow .

Small things do a lot for me .

[headslikekites]

I've only been a teto enjoyer for like a couple years now but it cannot be overstated the world she means to me

[palestine1989]

[bmpmp3]

passes away forever

[imarticuslearning-course]

Welcome to Imarticus Learning! In this video, we break down the most important data terms you need to know in Machine Learning. If you're just starting out or already working in the field, understanding these basic concepts is a must. You’ll learn what data means in Machine Learning, the difference between labeled and unlabeled data, and how to work with it. We also explain key ideas like features, labels, datasets, and data splits in a very simple way. You’ll get a clear picture of how data is prepared and used in real-world ML models. This is a great first step if you're looking for the best machine learning course that makes learning easy and useful. At Imarticus Learning, we focus on teaching with real examples, expert support, and flexible learning options to help you succeed in your career. Let’s start your journey into the world of AI and ML today!

[nuadox]

Researchers develop unsupervised machine learning method to improve fraud detection in imbalanced datasets

- By Nuadox Crew -

Researchers at Florida Atlantic University have developed a new machine learning method that significantly improves fraud detection by generating accurate class labels from severely imbalanced datasets—common in fraud cases where fraudulent events are rare.

Unlike traditional methods that rely on labeled data, their unsupervised technique works without prior labeling, cutting costs and addressing privacy concerns.

Tested on large real-world datasets (European credit card transactions and Medicare claims), the method outperformed the widely-used Isolation Forest algorithm by minimizing false positives and requiring less human oversight. It combines three unsupervised learning models with a percentile-gradient approach to isolate the most confidently identified fraud cases, enhancing accuracy and efficiency.

Published in the Journal of Big Data, this approach offers scalable, low-cost fraud detection for high-risk industries like finance and healthcare, and was recognized with a Best Student Paper Award at the IEEE ICTAI 2024 conference. Future work will focus on automating optimal label selection to further boost scalability.

Read more at Florida Atlantic University (FAU)

Scientific paper: Mary Anne Walauskis et al, Unsupervised label generation for severely imbalanced fraud data, Journal of Big Data (2025). DOI: 10.1186/s40537-025-01120-x

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[eastnetsblogs]

3 Benefits of Bank Fraud Detection Using Machine Learning

Bank fraud remains a growing concern in the financial sector, with criminals adopting increasingly sophisticated tactics. To stay ahead, financial institutions are turning to machine learning (machine learning) for fraud detection. This technology revolutionises how banks identify and prevent fraudulent activities. Here are three key benefits of using machine learning in fraud detection. 

1. Real-Time Fraud Detection:

Machine learning algorithms analyse massive amounts of data in real time, identifying suspicious patterns or anomalies as they occur. Unlike traditional methods, which often rely on retrospective analysis, machine learning offers proactive fraud detection. This swift response minimises financial losses and protects customer accounts from potential breaches. 

2. Improved Accuracy and Reduced False Positives:

Manual fraud detection methods can result in false positives, leading to customer dissatisfaction and operational inefficiencies. Machine learning models continuously learn and adapt to evolving fraud trends, enabling them to distinguish genuine transactions from fraudulent ones with greater precision. This reduces false alarms, allowing banks to focus resources on actual threats. 

3. Cost Efficiency and Scalability:

By automating fraud detection processes, machine learning reduces the reliance on manual interventions, cutting operational costs. Additionally, machine learning systems are scalable for banking fraud detection, making them suitable for banks of all sizes, whether handling a few transactions or millions daily. 

Embracing machine learning empowers banks to stay ahead of fraudsters, ensuring customer trust and operational efficiency in an ever-evolving financial landscape. The best you can do is to get connected with a reliable software provider. Request a demo today!

[legendaryearthquakestranger]

AI technology and the future of humans

AI technology and the future of humans: Artificial Intelligence (AI) has rapidly evolved from a futuristic concept to an integral part of our daily lives.

AI technology and the future of humans: Artificial Intelligence (AI) has rapidly evolved from a futuristic concept to an integral part of our daily lives. From smart home devices to advanced data analytics, AI is revolutionizing various sectors and reshaping how we interact with technology. In this article, we’ll explore the rise of AI, its applications, and how it’s transforming our…

[brillioitservices]

The Generative AI Revolution: Transforming Industries with Brillio

The realm of artificial intelligence is experiencing a paradigm shift with the emergence of generative AI. Unlike traditional AI models focused on analyzing existing data, generative AI takes a leap forward by creating entirely new content. The generative ai technology unlocks a future brimming with possibilities across diverse industries. Let's read about the transformative power of generative AI in various sectors: 

1. Healthcare Industry: 

AI for Network Optimization: Generative AI can optimize healthcare networks by predicting patient flow, resource allocation, etc. This translates to streamlined operations, improved efficiency, and potentially reduced wait times. 

Generative AI for Life Sciences & Pharma: Imagine accelerating drug discovery by generating new molecule structures with desired properties. Generative AI can analyze vast datasets to identify potential drug candidates, saving valuable time and resources in the pharmaceutical research and development process. 

Patient Experience Redefined: Generative AI can personalize patient communication and education. Imagine chatbots that provide tailored guidance based on a patient's medical history or generate realistic simulations for medical training. 

Future of AI in Healthcare: Generative AI has the potential to revolutionize disease diagnosis and treatment plans by creating synthetic patient data for anonymized medical research and personalized drug development based on individual genetic profiles. 

2. Retail Industry: 

Advanced Analytics with Generative AI: Retailers can leverage generative AI to analyze customer behavior and predict future trends. This allows for targeted marketing campaigns, optimized product placement based on customer preferences, and even the generation of personalized product recommendations. 

AI Retail Merchandising: Imagine creating a virtual storefront that dynamically adjusts based on customer demographics and real-time buying patterns. Generative AI can optimize product assortments, recommend complementary items, and predict optimal pricing strategies. 

Demystifying Customer Experience: Generative AI can analyze customer feedback and social media data to identify emerging trends and potential areas of improvement in the customer journey. This empowers retailers to take proactive steps to enhance customer satisfaction and loyalty. 

3. Finance Industry: 

Generative AI in Banking: Generative AI can streamline loan application processes by automatically generating personalized loan offers and risk assessments. This reduces processing time and improves customer service efficiency. 

4. Technology Industry: 

Generative AI for Software Testing: Imagine automating the creation of large-scale test datasets for various software functionalities. Generative AI can expedite the testing process, identify potential vulnerabilities more effectively, and contribute to faster software releases. 

Generative AI for Hi-Tech: This technology can accelerate innovation in various high-tech fields by creating novel designs for microchips, materials, or even generating code snippets to enhance existing software functionalities. 

Generative AI for Telecom: Generative AI can optimize network performance by predicting potential obstruction and generating data patterns to simulate network traffic scenarios. This allows telecom companies to proactively maintain and improve network efficiency. 

5. Generative AI Beyond Industries: 

GenAI Powered Search Engine: Imagine a search engine that understands context and intent, generating relevant and personalized results tailored to your specific needs. This eliminates the need to sift through mountains of irrelevant information, enhancing the overall search experience. 

Product Engineering with Generative AI: Design teams can leverage generative AI to create new product prototypes, explore innovative design possibilities, and accelerate the product development cycle. 

Machine Learning with Generative AI: Generative AI can be used to create synthetic training data for machine learning models, leading to improved accuracy and enhanced efficiency. 

Global Data Studio with Generative AI: Imagine generating realistic and anonymized datasets for data analysis purposes. This empowers researchers, businesses, and organizations to unlock insights from data while preserving privacy. 

6. Learning & Development with Generative AI: 

L&D Shares with Generative AI: This technology can create realistic simulations and personalized training modules tailored to individual learning styles and skill gaps. Generative AI can personalize the learning experience, fostering deeper engagement and knowledge retention. 

HFS Generative AI: Generative AI can be used to personalize learning experiences for employees in the human resources and financial services sector. This technology can create tailored training programs for onboarding, compliance training, and skill development. 

7. Generative AI for AIOps: 

AIOps (Artificial Intelligence for IT Operations) utilizes AI to automate and optimize IT infrastructure management. Generative AI can further enhance this process by predicting potential IT issues before they occur, generating synthetic data for simulating scenarios, and optimizing remediation strategies. 

Conclusion: 

The potential of generative AI is vast, with its applications continuously expanding across industries. As research and development progress, we can expect even more groundbreaking advancements that will reshape the way we live, work, and interact with technology. 

Reference- https://articlescad.com/the-generative-ai-revolution-transforming-industries-with-brillio-231268.html 

[daas4pro]

Can Blockchain Technology replace Chartered Accountants?

Auditing is undergoing a seismic shift, largely driven by the relentless march of blockchain technology. Blockchain technology is not replacing auditors; it’s empowering chartered accountants. The future of audit lies in a collaborative approach. Technology like blockchain handles tedious tasks like reconciliation through secure, shared ledgers. This frees up CAs from the time-consuming task of ensuring data accuracy, allowing them to focus on higher-level analysis and providing valuable insights that directly impact client decision-making.

This powerful synergy between Chartered Accountants and blockchain technology unlocks a new era of auditing:

⏩Enhanced Efficiency: Streamlined processes through automated tasks like reconciliation free up valuable Chartered Accountant time. This translates to faster audits and reduced costs for both CAs and their clients.

⏩Effective Risk Management: Real-time access to secure blockchain data allows for continuous auditing. This empowers Chartered Accountants to identify potential issues early on, enabling proactive risk management for clients.

⏩Perceptive Analysis: Chartered Accountants leverage their expertise to interpret the vast amounts of secure data generated by blockchain. This deeper financial health insight empowers them to provide clients with more perceptive analysis and strategic guidance.

This collaborative approach ultimately strengthens trust and transparency in the financial markets. Ready to Lead the Audit Revolution? Learn how Blockchain Empowers Chartered Accountants

[littlefankingdom]

Bruce is a overprotective and kind of strict parent, but he is very lax in some domains where other parents wouldn't. Here are some examples:

His kids stealing money from him. You will never catch Bruce Wayne lecturing his kids for taking his money. In the Arkhamverse, Jason steals 5 millions from Bruce's bank account to buy his army, and the problems for Bruce are: he didn't know it was Jason so it stressed him a bit, and Jason used it to buy an army.

Stealing from him in general. What is his is theirs. Unless it's dangerous. (Cars are death machines for his anxious self, which is why buying another batmobile for the young justice is not acceptable, or is kids taking it for a ride. He did made Redbird for Tim as a gift for when he got his license.)

Stealing from the cops (he has done it himself so many times)

Stealing money from rich people. In Knightfall, Bruce meets a British vigilante named Hood who steals from the rich to give to the poor, and Bruce had NO problems with that. He likes the young man. Stealing possessions is an issue tho. (Dick should follow his Robin Hood's dream, his father is fine with that)

Hacking into government facilities or anything really. Unless it's to harm an innocent civilian, like a classmate, he will not say anything. Hacking the FBI? Good. Hacking a russian mafia? Ok. As long as they do it safely and follow Barbara's instructions, it's fine.

Lying to him. Bruce is always impressed when he realizes one of his kids lied to him and he believed them. He's the Batman, after all, they have been able to fool the Batman. When he learns that Tim invented a fake uncle, he is proud of him and he tells him such, because he made the Batman believed it.

[zrixinc]

Software developers are implementing. advanced cybersecurity measures such as data encryption and secure transactions.