#Big Data in Finance

[techtoio]

The Impact of Big Data Analytics on Business Decisions

Introduction

Big data analytics has transformed the way of doing business, deciding, and strategizing for future actions. One can harness vast reams of data to extract insights that were otherwise unimaginable for increasing the efficiency, customer satisfaction, and overall profitability of a venture. We steer into an in-depth view of how big data analytics is equipping business decisions, its benefits, and some future trends shaping up in this dynamic field in this article. Read to continue

[truetechreview]

How DeepSeek AI Revolutionizes Data Analysis

1. Introduction: The Data Analysis Crisis and AI’s Role2. What Is DeepSeek AI?3. Key Features of DeepSeek AI for Data Analysis4. How DeepSeek AI Outperforms Traditional Tools5. Real-World Applications Across Industries6. Step-by-Step: Implementing DeepSeek AI in Your Workflow7. FAQs About DeepSeek AI8. Conclusion 1. Introduction: The Data Analysis Crisis and AI’s Role Businesses today generate…

[capitalism-and-analytics]

For anyone currently in school or recently in school

I am doing a few guest lectures at some of the universities in my state and working on my presentation. Curious if anyone has any recommendations of topics they really enjoyed from a past guest speaker that isn't major/field-specific?

[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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[bigulalgotrading]

How Big Data is Revolutionizing Algorithmic Trading | Bigul

Big data, AI, and real time processing are transforming algorithmic trading. Explore its future with quantum computing and blockchain for smarter decisions.

Read more..

[curiousquill1]

How Portfolio Management Firms Use Advanced Data Analytics to Transform Investment Strategies

Portfolio management firms are experiencing an innovative shift in how they make funding selections. Gone are the days of gut-feeling investments and conventional stock-picking methods. Today's wealth management firms are harnessing the notable electricity of statistics analytics to create extra sturdy, sensible, and strategically sound investment portfolio management procedures.

The Financial Landscape: Why Data Matters More Than Ever

Imagine navigating a complicated maze blindfolded. That's how investment decisions used to feel earlier than the data revolution. Portfolio control corporations now have access to unheard-of stages of facts, remodelling blind guesswork into precision-centered strategies.

The international economic actions are lightning-fast. Market conditions can change in milliseconds, and traders need partners who can adapt quickly. Sophisticated information analysis has grown to be the cornerstone of a successful funding portfolio control, permitting wealth control corporations to:

Predict market trends with first-rate accuracy.

Minimize chance via comprehensive data modelling.

Create personalized funding strategies tailor-made to your wishes.

Respond to worldwide economic shifts in close to actual time.

The Data-Driven Approach: How Modern Firms Gain an Edge

Top-tier portfolio control corporations aren't simply amassing records—they are interpreting them intelligently. Advanced algorithms and machine-learning techniques permit these corporations to gather large amounts of facts from more than one asset, inclusive of:

Global marketplace indexes

Economic reviews

Corporate economic statements

Geopolitical news and developments

Social media sentiment analysis

By integrating these diverse record streams, wealth management corporations can develop nuanced investment strategies that move a ways past conventional economic analysis.

Real-World Impact: A Case Study in Smart Data Usage

Consider a mid-sized portfolio management firm that transformed its approach via strategic statistics utilization. Imposing superior predictive analytics, they reduced customer portfolio volatility by 22%, even as they preserved competitive returns. This is not simply variety-crunching—it's approximately offering true monetary protection and peace of mind.

Key Factors in Selecting a Data-Driven Portfolio Management Partner

When evaluating investment portfolio management offerings, sophisticated traders should search for companies that demonstrate

Transparent Data Methodologies: Clear reasons for ways information influences funding decisions

Cutting-Edge Technology: Investment in superior predictive analytics and system mastering

Proven Track Record: Demonstrable achievement in the use of facts-pushed strategies

Customisation Capabilities: Ability to tailor techniques to individual risk profiles and monetary goals

The Human Touch in a Data-Driven World

While data analytics presents powerful insights, the most successful portfolio control firms firmsrecognizee that generation complements—however in no way replaces—human knowledge. Expert monetary analysts interpret complicated fact patterns, including critical contextual knowledge that raw algorithms cannot.

Emotional Intelligence Meets Mathematical Precision

Data does not simply represent numbers; it tells testimonies about financial landscapes, enterprise tendencies, and ability opportunities. The best wealth control firms translate these records and memories into actionable, personalized investment techniques.

Making Your Move: Choosing the Right Portfolio Management Partner

Selecting a portfolio control firm is a deeply personal selection. Look beyond flashy advertising and marketing and observe the firm's proper commitment to records-pushed, wise investment techniques. The right companion will offer:

Comprehensive statistics evaluation

Transparent communication

Personalised investment approaches

Continuous strategy optimisation

Final Thoughts: The Future of Intelligent Investing

Portfolio control firms standing at the forefront of the data revolution are rewriting the guidelines of the funding method. By combining advanced technological abilities with profound financial understanding, those companies provide buyers something that is, in reality, transformative: self-assurance in an unsure monetary world.

The message is obvious: in current investment portfolio management, facts aren't always simply information—they are the important thing to unlocking unparalleled financial potential.

[river-taxbird]

AI hasn't improved in 18 months. It's likely that this is it. There is currently no evidence the capabilities of ChatGPT will ever improve. It's time for AI companies to put up or shut up.

I'm just re-iterating this excellent post from Ed Zitron, but it's not left my head since I read it and I want to share it. I'm also taking some talking points from Ed's other posts. So basically:

We keep hearing AI is going to get better and better, but these promises seem to be coming from a mix of companies engaging in wild speculation and lying.

Chatgpt, the industry leading large language model, has not materially improved in 18 months. For something that claims to be getting exponentially better, it sure is the same shit.

Hallucinations appear to be an inherent aspect of the technology. Since it's based on statistics and ai doesn't know anything, it can never know what is true. How could I possibly trust it to get any real work done if I can't rely on it's output? If I have to fact check everything it says I might as well do the work myself.

For "real" ai that does know what is true to exist, it would require us to discover new concepts in psychology, math, and computing, which open ai is not working on, and seemingly no other ai companies are either.

Open ai has already seemingly slurped up all the data from the open web already. Chatgpt 5 would take 5x more training data than chatgpt 4 to train. Where is this data coming from, exactly?

Since improvement appears to have ground to a halt, what if this is it? What if Chatgpt 4 is as good as LLMs can ever be? What use is it?

As Jim Covello, a leading semiconductor analyst at Goldman Sachs said (on page 10, and that's big finance so you know they only care about money): if tech companies are spending a trillion dollars to build up the infrastructure to support ai, what trillion dollar problem is it meant to solve? AI companies have a unique talent for burning venture capital and it's unclear if Open AI will be able to survive more than a few years unless everyone suddenly adopts it all at once. (Hey, didn't crypto and the metaverse also require spontaneous mass adoption to make sense?)

There is no problem that current ai is a solution to. Consumer tech is basically solved, normal people don't need more tech than a laptop and a smartphone. Big tech have run out of innovations, and they are desperately looking for the next thing to sell. It happened with the metaverse and it's happening again.

In summary:

Ai hasn't materially improved since the launch of Chatgpt4, which wasn't that big of an upgrade to 3.

There is currently no technological roadmap for ai to become better than it is. (As Jim Covello said on the Goldman Sachs report, the evolution of smartphones was openly planned years ahead of time.) The current problems are inherent to the current technology and nobody has indicated there is any way to solve them in the pipeline. We have likely reached the limits of what LLMs can do, and they still can't do much.

Don't believe AI companies when they say things are going to improve from where they are now before they provide evidence. It's time for the AI shills to put up, or shut up.

[mobio-solutions]

In today's fast-paced financial world, understanding the power of big data is more crucial than ever. 📊 We're diving into how big data is reshaping finance, offering unprecedented insights and opening new doors of opportunity.

Whether you're a seasoned investor or just curious about market trends, our latest "Big Data in Finance" discussion is your gateway to staying ahead.

Join us as we explore the exciting intersection of technology and finance and discover how big data is not just a buzzword but a key to unlocking potential in today's market.📈

[ramakantthinks]

The Synergy of Big Data and Finance Insights

In the dynamic world of finance, knowledge is power, and data is its lifeblood. Imagine a treasure trove of information at your fingertips, revealing hidden patterns, predicting market trends, and guiding your financial decisions like never before. That's the magic of leveraging Big Data in the finance sector. Today, we embark on an exciting journey to explore how Big Data is revolutionizing the financial landscape in India.

The Big Data Boom

You may wonder, what exactly is Big Data? Well, it's like a gigantic web capturing every digital move we make. From online transactions and social media posts to website visits and smartphone usage – Big Data is everywhere. In India, with the massive surge in internet usage and smartphone adoption, we generate an ocean of data daily. Leveraging this data intelligently can unlock a whole new world of finance insights.

Also Read: The Rise of Decentralized Finance (DeFi): Exploring the Opportunities and Risks of a Borderless Financial Ecosystem

Data-Driven Personal Finance

Managing personal finances can be a daunting task, especially with so many investment options and financial products available. But fear not, as Big Data comes to the rescue! With advanced analytics, financial institutions can analyze individual spending habits, investment preferences, and risk appetite. Armed with this information, personalized financial advice and tailored investment plans can be offered to every Indian, helping them achieve their financial goals.

Transforming Credit Assessment

In the past, getting a loan was akin to running a marathon, slow and exhausting. But Big Data has changed the game! Traditional credit scoring models are now combined with alternative data sources like mobile phone usage and social media activity. As a result, lenders can assess creditworthiness more accurately and provide loans faster. This is a boon for many Indians, especially those who lack a robust credit history.

Predicting Market Trends

Investing in the stock market is like riding a roller coaster – thrilling but unpredictable. Big Data has made this ride a lot smoother! By analyzing vast amounts of market data, including news sentiment, social media chatter, and historical trends, financial analysts can make more informed predictions. Investors in India can now make smarter decisions, minimizing risks, and maximizing returns.

Curbing Financial Frauds

Financial fraud is an unfortunate reality that plagues the global financial system. However, Big Data is proving to be a formidable weapon against fraudsters. Advanced algorithms analyze transactions in real-time, flagging suspicious activities and preventing potential fraud. As Indians increasingly embrace digital transactions, this technology becomes crucial in safeguarding our hard-earned money.

AI-Powered Chatbots

Ever wished for a financial advisor who is available 24/7? Well, now you can have one! Big Data, coupled with Artificial Intelligence, has given birth to intelligent chatbots. These virtual assistants can answer queries, provide personalized financial advice, and even execute trades, all in real time. The convenience and accessibility of chatbots empower millions of Indians to make better financial decisions.

Enhancing Risk Management

Managing financial risks is paramount, and Big Data offers an invaluable advantage. Financial institutions in India can now assess risks more accurately by integrating internal data with external sources like economic indicators and market data. This comprehensive risk analysis ensures a robust and stable financial system, benefiting both institutions and customers.

Also Read: The Role of AI and Machine Learning in Financial Decision Making

Conclusion

The marriage of Big Data and finance is transforming the way Indians manage their money. From personalized financial advice to predicting market trends and curbing fraud, the power of data is revolutionizing the financial landscape. As we embrace this data-driven revolution, let's remember to use this technology responsibly, ensuring a brighter and more prosperous financial future for all.

[capitalism-and-analytics]

[p-p-c]

Fortifying the Financial Fortress with 5 Battle-Tested Strategies

In 2022, the financial sector experienced more than 3,500 data breaches, resulting in a crazy 10 billion records being compromised. And the impact is not just limited to corporates; it reaches us, the consumers, too. So, where are the weak spots? How is it possible that these financial giants struggle to identify them while cybercriminals thrive? Is there a gap in security, or are the security mechanisms themselves flawed? Let's dig deep and uncover the answers to these pressing questions.

Read article - https://bit.ly/3PndiqC

[mostlysignssomeportents]

Ad-tech targeting is an existential threat

I'm on a 20+ city book tour for my new novel PICKS AND SHOVELS. Catch me TORONTO on SUNDAY (Feb 23) at Another Story Books, and in NYC on WEDNESDAY (26 Feb) with JOHN HODGMAN. More tour dates here.

The commercial surveillance industry is almost totally unregulated. Data brokers, ad-tech, and everyone in between – they harvest, store, analyze, sell and rent every intimate, sensitive, potentially compromising fact about your life.

Late last year, I testified at a Consumer Finance Protection Bureau hearing about a proposed new rule to kill off data brokers, who are the lynchpin of the industry:

https://pluralistic.net/2023/08/16/the-second-best-time-is-now/#the-point-of-a-system-is-what-it-does

The other witnesses were fascinating – and chilling, There was a lawyer from the AARP who explained how data-brokers would let you target ads to categories like "seniors with dementia." Then there was someone from the Pentagon, discussing how anyone could do an ad-buy targeting "people enlisted in the armed forces who have gambling problems." Sure, I thought, and you don't even need these explicit categories: if you served an ad to "people 25-40 with Ivy League/Big Ten law or political science degrees within 5 miles of Congress," you could serve an ad with a malicious payload to every Congressional staffer.

Now, that's just the data brokers. The real action is in ad-tech, a sector dominated by two giant companies, Meta and Google. These companies claim that they are better than the unregulated data-broker cowboys at the bottom of the food-chain. They say they're responsible wielders of unregulated monopoly surveillance power. Reader, they are not.

Meta has been repeatedly caught offering ad-targeting like "depressed teenagers" (great for your next incel recruiting drive):

https://www.technologyreview.com/2017/05/01/105987/is-facebook-targeting-ads-at-sad-teens/

And Google? They just keep on getting caught with both hands in the creepy commercial surveillance cookie-jar. Today, Wired's Dell Cameron and Dhruv Mehrotra report on a way to use Google to target people with chronic illnesses, people in financial distress, and national security "decision makers":

https://www.wired.com/story/google-dv360-banned-audience-segments-national-security/

Google doesn't offer these categories itself, they just allow data-brokers to assemble them and offer them for sale via Google. Just as it's possible to generate a target of "Congressional staffers" by using location and education data, it's possible to target people with chronic illnesses based on things like whether they regularly travel to clinics that treat HIV, asthma, chronic pain, etc.

Google claims that this violates their policies, and that they have best-of-breed technical measures to prevent this from happening, but when Wired asked how this data-broker was able to sell these audiences – including people in menopause, or with "chronic pain, fibromyalgia, psoriasis, arthritis, high cholesterol, and hypertension" – Google did not reply.

The data broker in the report also sold access to people based on which medications they took (including Ambien), people who abuse opioids or are recovering from opioid addiction, people with endocrine disorders, and "contractors with access to restricted US defense-related technologies."

It's easy to see how these categories could enable blackmail, spear-phishing, scams, malvertising, and many other crimes that threaten individuals, groups, and the nation as a whole. The US Office of Naval Intelligence has already published details of how "anonymous" people targeted by ads can be identified:

https://www.odni.gov/files/ODNI/documents/assessments/ODNI-Declassified-Report-on-CAI-January2022.pdf

The most amazing part is how the 33,000 targeting segments came to public light: an activist just pretended to be an ad buyer, and the data-broker sent him the whole package, no questions asked. Johnny Ryan is a brilliant Irish privacy activist with the Irish Council for Civil Liberties. He created a fake data analytics website for a company that wasn't registered anywhere, then sent out a sales query to a brokerage (the brokerage isn't identified in the piece, to prevent bad actors from using it to attack targeted categories of people).

Foreign states, including China – a favorite boogeyman of the US national security establishment – can buy Google's data and target users based on Google ad-tech stack. In the past, Chinese spies have used malvertising – serving targeted ads loaded with malware – to attack their adversaries. Chinese firms spend billions every year to target ads to Americans:

https://www.nytimes.com/2024/03/06/business/google-meta-temu-shein.html

Google and Meta have no meaningful checks to prevent anyone from establishing a shell company that buys and targets ads with their services, and the data-brokers that feed into those services are even less well-protected against fraud and other malicious act.

All of this is only possible because Congress has failed to act on privacy since 1988. That's the year that Congress passed the Video Privacy Protection Act, which bans video store clerks from telling the newspapers which VHS cassettes you have at home. That's also the last time Congress passed a federal consumer privacy law:

https://en.wikipedia.org/wiki/Video_Privacy_Protection_Act

The legislative history of the VPPA is telling: it was passed after a newspaper published the leaked video-rental history of a far-right judge named Robert Bork, whom Reagan hoped to elevate to the Supreme Court. Bork failed his Senate confirmation hearings, but not because of his video rentals (he actually had pretty good taste in movies). Rather, it was because he was a Nixonite criminal and virulent loudmouth racist whose record was strewn with the most disgusting nonsense imaginable).

But the leak of Bork's video-rental history gave Congress the cold grue. His video rental history wasn't embarrassing, but it sure seemed like Congress had some stuff in its video-rental records that they didn't want voters finding out about. They beat all land-speed records in making it a crime to tell anyone what kind of movies they (and we) were watching.

And that was it. For 37 years, Congress has completely failed to pass another consumer privacy law. Which is how we got here – to this moment where you can target ads to suicidal teens, gambling addicted soldiers in Minuteman silos, grannies with Alzheimer's, and every Congressional staffer on the Hill.

Some people think the problem with mass surveillance is a kind of machine-driven, automated mind-control ray. They believe the self-aggrandizing claims of tech bros to have finally perfected the elusive mind-control ray, using big data and machine learning.

But you don't need to accept these outlandish claims – which come from Big Tech's sales literature, wherein they boast to potential advertisers that surveillance ads are devastatingly effective – to understand how and why this is harmful. If you're struggling with opioid addiction and I target an ad to you for a fake cure or rehab center, I haven't brainwashed you – I've just tricked you. We don't have to believe in mind-control to believe that targeted lies can cause unlimited harms.

And those harms are indeed grave. Stein's Law predicts that "anything that can't go on forever eventually stops." Congress's failure on privacy has put us all at risk – including Congress. It's only a matter of time until the commercial surveillance industry is responsible for a massive leak, targeted phishing campaign, or a ghastly national security incident involving Congress. Perhaps then we will get action.

In the meantime, the coalition of people whose problems can be blamed on the failure to update privacy law continues to grow. That coalition includes protesters whose identities were served up to cops, teenagers who were tracked to out-of-state abortion clinics, people of color who were discriminated against in hiring and lending, and anyone who's been harassed with deepfake porn:

https://pluralistic.net/2023/12/06/privacy-first/#but-not-just-privacy

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2025/02/20/privacy-first-second-third/#malvertising

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

[kravatkan]

Based on those lines from DE, I tried to estimate Harry's salary in USD, using prices in Ukraine as a starting-off point (pros of being Eastern European...?)

And I also estimated the price of Whirling's special borsht.

This is not a serious math solution; I took a lot of liberties to round the numbers and didn’t spend much time looking for equivalent products. I mostly settled for the cheapest items sold at big chains of supermarkets.

Methodology here is not great. This is just for fun. I'm not good at math and statistics, sorry TT

What I did:

found the UAH prices for analogues of some items sold in the game

averaged the UAH/reál ratio to estimate the price of 1 reál

then converted the final result to USD

Price of 1 reál

Starting from the game items to feature in those calculations, Frittte products are probably the easiest to find analogues for. As for pawn shop and book items from the game, it’d be hard to estimate, I think, the prices vary a lot. Especially for second-hand items that have lower prices.

FALN pants were an exception, because maybe Cuno sells you the new ones? I took the average price of sweatpants made by a local brand popular among young people.

With magnesium I’m also not sure, turns out it's rarely sold by itself without added minerals or vitamins, such as B6. Medications in Ukraine are most frequently sold in blisters, not small plastic bottles like the one in the game art, so I chose the price for a small tube of water-dissolvable tablets.

The price of Drouamine is taken from the price of Ibuprofen.

The final number was an average of all UAH prices divided by reál prices

So, with those loose calculations, the price of 1 reál is around 41,7 UAH, by today’s exchange rates it’s pretty close to 1 USD.

2. Harry's salary

This is the numbers we get.

With his monthly salary Harry can buy, uuuh, 91 bottles of Commodore Red or 152 bottles of Potent Pilsner or 19 112,50 hryvnias.

Just for comparison,

Data for salaries is taken from the biggest work searching website in the country. Couldn't find the median salaries, though.

The price of a motor carriage Harry destroyed is ~40 000 USD/1 668 000 UAH. It'd take him 7 years 4 months to repay.

3. Conclusions

For a competent detective with a high rank who spent 18 years of his life working in RCM? I thought it’d be more. For lower ranks it'd be much harder to get by, puts into perspective how he lived with Dora.

Vibe-based? Livable, especially if Harry owns an apartment and doesn’t have to pay rent. If we say he spends around half of his salary on rent+bills, he can survive on inexpensive food (if he wants to also by substances).

Genuinely scared of thinking how Jean lives with an even lower monthly pay. Judit has three children... oh god. I hope Harry bought them a lot of kebabs (the price of a very delicious big kebab a friend bought me once was 120 UAH /~3 USD).

4. Bonus: the price of Gorący's special borscht

That's right, we're so normal about borscht here that we have something called "Borscht index". It's the price of ingredients used to cook this essential dish that helps to track inflation.

Information from the Ministry of Finance of Ukraine (March, 2025).

If we say Gorący's pot is 20 L (19 L borscht + 1 L vodka), the price would be around 1394 UAH or 33 reál.

Unless he's cooking the Polish barszcz czerwony, which is pretty different from Ukrainian borscht, if I remember correctly.

So, yeah, if we use Ukraine as a stand-in Eastern European country for Revachol, it'd be like this. Thank you for reading! Sorry if the math is very bad TT