Big data processing for a bank loan approval: Why?
"Why do banks need data analytics?" "Today’s most competitive banks treat data as a core business asset, one that informs everything from loan approvals to fraud detection to long-term growth strategy." https://innowise.com/blog/data-analytics-in-banking/
Bankers presenting their Big Data Supply Chain Architecture: "Analytics 4 tomorrow".
The banker said "Anything that cannot be measured does not exist. To grant credit, we must measure and predict whether the customer will be able to repay it."
Big data processing for a bank loan approval: data sources
Use cases to user stories based on specifications and banker needs to meseaure the customer behavior.
In this case study, I understood after several readings, that my customer needed a mobile application to recover among other things geolocation, telemetry, etc., in short data on mobile phones ( "the front") and then send this information to the "backend", that is to say, a database. The front web/mobile application can be developed with any technology (Kotlin, Swift, React Native, Windev...)
Mobile phone applications: first analyze the need carefully
Imagine an application, we can call it "Whassup" and we can call the project "the molar mic"...
There is some "Whassup group", (work group, family group, leisure group...) composed of several members...". Technical expressions come up several times in meetings, such as:
Match behaviors, Predict Geo-locations, Proximite events, Report, Detect neighbors...
Trace routes, Reverse geocode to real address, Historise location, Detect motion, Encrypt data...
sends a push notification to the mobile device... sends a push notification to the servers... feed our database...
locate point-of-interest... describe the objects with similar tags that users have...
request for their future positions of users... Send encrypted tags... Send locations...
Send interests... Send matchinfs... Send POI infos...collect location history... collect paths...
collect journeys...Encrypt data to databases... Store Encryption Key on the telephone of the user...
Store Encryption Key in our in-house secret server...
Analysis and modeling phase
Smartphone applications: preliminary use cases list that will describe the functionalities to be developed
These information collections are generally carried out by applications on the phone, or "frontend". Some processing (aggregation of informations ) is carried out locally, others which are very greedy in terms of processor resources and therefore energy (intensive calculations with artificial intelligence) are done in the cloud so as not to drain the phone's battery.
Big data processing for a bank loan approval: data storage
Big Data architecture: it is the data source to go to later to the data storage (data lake...) of Azure Data lake storage, or OneLake in Microsoft Fabric: the datas on the phones will be sent to the Cloud Azure.
Smartphone applications: use cases concerning the hardware used
Google Android or Apple IOS phones.
Match behaviors
Predict Geo-locations
Proximite events
Report
Detect neighbors
Trace routes
Reverse geocode to real address
Historise location
Find the best itinerary
Find points of interests along the way
get notices about nearby friends
get notices about friends Location
deal with our current positions
offer information about places we have been
keep in touch with users we’ve met
Avoid filling interfaces as much as possible
forecast my trips
forecasting my future positions
Telling where i will be
Integrate predictive models into apps
Make matchings between mobile users
Make matchings between mobile users
Make matchings between points of interest
Activate geolocation functions with only a few line of code
API gives mobile developers access to geolocation functionality analytics
Provide geolocation
Provide routing
integrate the API with 6 lines of code
integrate the API in a few minutes
detect preferential moving targets
easily find neighbors nearby
proactively predict users’ movements
organize user meetings
offer ideal matches
be affordable to all developers
easily accessible to all developers.
estimate in real time where the user is actually going
estimate where the user will go next
estimate when the user will go next
Run geolocation in the mobile’s background
preserve the user’s battery
Notify the user
Detect motion
Encrypt data
Tell the developer to use a laptop
request for “matches” between users
request for “matches” between static points-of-interest
detects a match
sends a push notification to the mobile device
sends a push notification to the servers
match users using their past positions
match users using their current positions
match users using current destinations
match users using future proximity
match users using future intent-to-travel
Tag every user within his “Birthdate” parameter
Request matches of similarity for ages
Request matches of proximity of users
feed matching algorithms
match users with non-user objects
feed database
locate point-of-interest
describe the objects with similar tags that users have
request for matching between users
request for the future positions of users
Send encrypted tags
Send matchings
Send events
Send reports on matchings
Send locations
Send interests
Send POI infos
Send stocks
Accept that their personal data is required
Collects geolocation data
collect location history
collect paths
collect journeys
Encrypt data to databases
Store Encryption Key on the telephone
Store Encryption Key in in-house secret server
OpenSource IOS code
OpenSource ANDROID code
Notify the user of what we are doing with the geolocation
Show the user what is known about him
Delete all user data
Create white zones when application goes asleep
Make functions “ready to use”
Make functions “ customizable ”
Make applicaiton in native iOS (Swift and ObjectiveC)
Make application in native Android (Java)
Embed application with a few lines (duplicate use case)
geo-track in the background
Make all the self-dependent functions work immediately
Generate API Key
Provide code examples
Use the interface
add points-of-interest and tag them with your specific application data
request information about users (geolocations, geo-predictions, geo-correlations)
request for backend notifications in case of events or matches
trigger events into your application mobile nodes
request for user-privacy data to be fully removed from our backend
Use console
generate and manage API keys for each of your applications
visualize the movements of your users (or groups of users) over time
test and validate Geo-correlation matching syntax
Smartphone applications: use cases concerning the connected user
Location
Age
Generation
Gender
Language
Education level
Field of study
School
Ethnic affinity
Income and net worth
Home ownership and type
Home value
Property size
Square footage of home
Year home was built
Household composition
Users who have an anniversary within 30 days
Users who are away from family or hometown
Users who are friends with someone who has an anniversary, is newly married or engaged, recently moved, or has an upcoming birthday
Users in long-distance relationships
Users in new relationships
Users who have new jobs
Users who are newly engaged
Users who are newly married
Users who have recently moved
Users who have birthdays soon
Parents
...Could be like...
( Disney Snow White and the 7 dwarfs, Blanche Neige et les 7 nains. The Poisoned Apple. The Evil Queen offering up the poisoned apple: "And because you've been so good to poor old Granny, I'll share a secret with you. This is no ordinary apple. It's a magic wishing apple.". She is incredibly jealous of Snow White’s beauty and can’t see how her own ugliness is more than just skin deep. A cruel and iconic scene.)
Expectant parents
Mothers, divided by “type” (soccer, trendy, etc.)
Users who are likely to engage in politics
Conservatives and liberals
Relationship status
Employer
Industry
Job title
Office type
Interests
Users who own motorcycles
Users who plan to buy a car (and what kind/brand of car, and how soon)
Users who bought auto parts or accessories recently
Users who are likely to need auto parts or services
Style and brand of car you drive
Year car was bought
Age of car
How much money user is likely to spend on next car
Where user is likely to buy next car
How many employees your company has
Users who own small businesses
Users who work in management or are executives
Users who have donated to charity (divided by type)
Operating system
Users who play canvas games
Users who own a gaming console
Users who have created a [Framework Name] event
Users who have used [Framework Name] Payments
Users who have spent more than average on [Framework Name] Payments
Users who administer a [Framework Name] page
Users who have recently uploaded photos to [Framework Name]
Internet browser
Email service
Early/late adopters of technology
Expats (divided by what country they are from originally)
Users who belong to a credit union, national bank or regional bank
Users who investor (divided by investment type)
Number of credit lines
Users who are active credit card users
Credit card type
Users who have a debit card
Users who carry a balance on their credit card
Users who listen to the radio
Preference in TV shows
Users who use a mobile device (divided by what brand they use)
Internet connection type
Users who recently acquired a smartphone or tablet
Users who access the Internet through a smartphone or tablet
Users who use coupons
Types of clothing user’s household buys
Time of year user’s household shops most
Users who are “heavy” buyers of beer, wine or spirits
Users who buy groceries (and what kinds)
Users who buy beauty products
Users who buy allergy medications, cough/cold medications, pain relief products, and over-the-counter meds
Users who spend money on household products
Users who spend money on products for kids or pets, and what kinds of pets
Users whose household makes more purchases than is average
Users who tend to shop online (or off)
Types of restaurants user eats at
Kinds of stores user shops at
Users who are “receptive” to offers from companies offering online auto insurance, higher education or mortgages, and prepaid debit cards/satellite TV
Length of time user has lived in house
Users who are likely to move soon
Users who are interested in the Olympics, fall football, cricket or Ramadan
Users who travel frequently, for work or pleasure
Users who commute to work
Types of vacations user tends to go on
Users who recently returned from a trip
Users who recently used a travel app
Users who participate in a timeshare
To create an application, you need to understand what the customer wants, analyze it to model it, and then develop it
"Information transmitted via social media on the Internet is harvested by several large companies." "The American company Palantir suspected of creating a giant database on citizens"
"L’entreprise américaine Palantir suspectée de créer une base de données géante sur les citoyens" And "Trump Taps Palantir to Compile Data on Americans" https://www.nytimes.com/2025/05/30/technology/trump-palantir-data-americans.html
Example of a giant database ( https://learn.microsoft.com/fr-fr/graph/overview ) Your administrator can help you to solve any problems, because he knows ALL about users/applications activity within the company (and so does the boss and the human resources) "System Administrator(s) or Power User' roles that usually have privileged accounts, accesses and considering at any point of time the system administrator(s) can do any unauthorized activities and get away with it without being detected because appropriate monitoring is not in place" https://learn.microsoft.com/en-us/answers/questions/5218289/how-an-administrator-in-microsoft-can-spy-snoop-on
"Palantir is worth 200 to 300 times its earnings, which is obviously absurd", " La DGSI vient de renouveler son contrat avec Palantir. " Gilles Babinet.
Big data processing for a bank loan approval:to provide funds for the project
"Meet the CIA-backed venture fund behind Palantir, Anduril—and a spy tool that might be on your phone"
In-Q-Tel was founded in 1999 by the CIA—yes that CIA, the Central Intelligence Agency—with the mission of closing a perceived innovation gap between Washington’s security establishment and Silicon Valley. Inventions like the Molar Mic are the reason In-Q-Tel exists. Over its 26 years in business, the fund has helped launch more than 800 companies. Of the companies in this year’s NatSec 100 Report, an annual index of the fastest-growing venture-backed defense startups, In-Q-Tel is an investor in 32—far more than any other fund."
Some of the companies the fund backs are publicly known, others are secret—as is the total amount of money In-Q-Tel has invested since it began. (Fortune’s estimate, informed by the last 25 years of In-Q-Tel’s tax disclosures, is at least $1.8 billion and likely more. But In-Q-tel itself, and sources close to it, declined to share or discuss any numbers.) Whatever the amount, by and large, the companies In-Q-Tel picks are all building technologies judged to be vital for U.S. national security..."
In-Q-Tel also backed Palantir, supplier of big-data analytics to the military and intel agencies. Palantir may well be the clearest sign that Silicon Valley has shacked up with the Pentagon for good: It was recently valued at $250 billion, surpassing traditional defense contracting titans like Northrup Grumman, Lockheed Martin, and General Dynamics, even though its revenue, at under $3 billion last year, is just a fraction of that of those giants."
https://finance.yahoo.com/news/meet-cia-backed-venture-fund-100000286.html
https://fortune.com/2025/07/29/in-q-tel-cia-venture-capital-palantir-anduril/
"It is also possible to carry out more in-depth analyses to understand lifestyle and travel habits" (For example, you spend 8 hours a day at the office, you sleep 8 hours a day at home, you travel 1 hour morning and evening between your home and the office, etc.)
https://www.chapsvision-cybergov.fr/solution/interception-judiciaire/
Souriez, vous êtes embeddés avec les algorithmes (Gaia-ID, Hulk, MindReader...)
This means that each action on a social network (example: launching the application, time spent viewing a video, clicking "like" on a publication, etc.) enriches the embedding profile enriches the embedding profile (https://www.abondance.com/20250722-1276969-revelations-fonctionnement-google.html ) ,
"software used to identify users and graph their social relationships."
"Almost every time a user sees an advertisement (in an app or in a web browser), the ad network is tracking users in the same way. "
Big data processing for a bank loan approval: credit cards linked to advertisements services
Google Ads, formerly known as Google Adwords, is an online advertising platform developed by Google, where advertisers bid to display brief digital advertisements service https://en.wikipedia.org/wiki/Google_Ads https://www.bloomberg.com/news/articles/2018-08-30/google-and-mastercard-cut-a-secret-ad-deal-to-track-retail-sales "Google reportedly bought Mastercard data to link online ads with offline purchases" https://www.theverge.com/2018/8/30/17801880/google-mastercard-data-online-ads-offline-purchase-history-privacy
"accurately detects whether you are IN_VEHICLE, ON_BICYCLE, ON_STAIRS, IN_ELEVATOR, or even SLEEPING" https://www.abondance.com/20250722-1276969-revelations-fonctionnement-google.html
"Every 15 minutes," they say, the 'Pixel 9 Pro XL sends a data packet to Google. The device shares location, email address, phone number, network status, and other telemetry data." They note that this data is sent "to various Google endpoints, including device management, policy enforcement, and face grouping.", Google tracks your location every 15 minutes even if the GPS is turned off; your mobile phone communicates with Google's services without your explicit consent
https://en.wikipedia.org/wiki/Pixel_9which will be saved, for example, in a vector database "vector embeddings" such as type Cosmos DB hosted on Azure.
...
"Running red lights on a person's credit report."
"Playing loud music or eating on public transportation"...
"Making reservations at restaurants or hotels but not showing up"
"Failing to properly sort personal waste"...
"Fraudulently using other people's public transportation ID cards"...
Ethical processing and GDPR compliance: Before implementing such processing, called "personal data points", we must ask ourselves whether it is ethical for a bank to base its lending policy on someone's race? Where they live? Their health status? What is the difference between personalization and discrimination?
This data collection (Google Tag Manager gtag.js, appsflyer...) is then consolidated by tools and transferred to the server, also called "backend" (SQL DW or SQL Datawarehouse, PolyBase, Azure Synapse Analytics, PostgreSQL, Mysql, MariaDB, etc.) through the network using specific protocols (look at the course on the web services) and securely thanks to cybersecurity and encryption in order to use the fantastic artificial intelligence algorithms of the Azure cloud.
Big data processing for a bank loan approval: Big data see what you see
Apple CSAM project
Every photo you take with your phone is sent to the cloud (Azure or whatever...) for analysis.
"on Apple devices, computation is done that... this entire process is triggered upon UPLOADING to iPhoto cloud"
"The vast majority of the tech industry uses photoDNA a technology developed by Microsoft
- Used by Law Enforcement, NCMEC/VIC, Microsoft, Google, Reddit, Discord, Adobe, Facebook, Twitter, etc.
- Like Apple’s system uses a “fingerprint”
- Extends to video by taking a key frame from each scene
- Google/Youtube have a proprietary ML system known as CSAI match
- Also used by Adobe, Twitter, Reddit"
https://interface.cs.princeton.edu/archive/2021-09-20_Apple-CSAM.pdf
PhotoDNA is available as a service on Azureavailable as a service on Azure
Each photo is transformed in keywords and analysed
Here, your phone saw a car, a taxi, a person, a dog, thanks to Azure computer vision...
https://www.consultingit.fr/fr/azure-ai-vision-sdk-for-image-analysis-explained-with-github-copilot
Look at the camera on the top of your phone and say cheeeeeeese
Big data processing for a bank loan approval: batch processing
Smartphone apps: towards the "backend" for intensive treatments
Because the datasets are massive, data files are typically handled by big data solutions through extended batch jobs that perform filtering, aggregation, and other preparation steps for analysis. These jobs generally consist of reading the original files, executing processing tasks, and generating new output files.
"with Azure’s near-limitless storage capacity, Unit 8200 began building a powerful new mass surveillance tool: a sweeping and intrusive system that collects and stores recordings of millions of mobile phone calls "
To accomplish this, you have the following options:
-
In Azure Databricks notebooks, you can write code using Python, Scala, or SQL.
-
In Fabric notebooks, you can also use Python, Scala, or SQL to process the data.
- In Dremio...
Dremio "Dremio is an open data lakehouse, providing self-service SQL analytics, data warehouse performance and functionality, and data lake flexibility across all of your data. Dremio increases agility with a revolutionary data-as-code approach that adopts Git concepts to enable data experimentation, version control, and governance. In addition, Dremio breaks down data silos by simplifying ingestion into the lakehouse, and enabling queries directly on databases and data warehouses.
The Dremio Azure application can be found in the Azure marketplace: "Azure applications"
https://marketplace.microsoft.com/fr-fr/product/azure-applications/dremiocorporation.dremio_ce
Dremio runs directly on ADLS, and is based on open-source technologies like Apache Arrow and Apache Iceberg, so you maintain control over your data."
https://marketplace.microsoft.com/fr-fr/product/azure-applications/dremiocorporation.dremio_ce
Now with a MCP server: https://github.com/dremio/dremio-mcp
2017 revolution: we can now use SQL queries in big Data architectures, thanks to Apache Iceberg which introduced ACID.
See the github of Apache Iceberg implementation: https://github.com/apache/iceberg
This is the Java implementation.
Big data processing: URSSAF too
By the way, URSSAF is using Big Data processing, with products like Dremio, Apache Iceberg, Parquet, PostgreSQL, Java, Python, Spark, and so and so...
URSSAF presenting his Big Data Fabrique
In France, everyone knows about URSSAF.
"We are URSAFF, CANCRAS, and CARBALAS, Whoever you are, whatever you do, You have to spit, you have to pay, There's no way you'll escape, We are the brothers who rap everything."
"Nous sommes URSAFF, CANCRAS et CARBALAS, Qui que tu sois, quoi que tu fasses, Faut qu'tu craches, faut qu'tu payes, Pas possible que t'en réchappes, Nous sommes les frères qui rappent tout."
For those who are not familiar with URSSAF, see here: https://fr.wikipedia.org/wiki/Rap-Tout https://en.wikipedia.org/wiki/URSSAF
The back-end will process this information by cross-referencing it with "Big Data" using query engines (Apache flink, Apache Spark, Trino...) on catalogs (Apache Polaris...) and data science (machine learning algorithms Spark, Hadoop, etc.) with SQL language ou langage python for example, even the famous Excel software, to then perform segmentations (RFM Recency, Frequency, Amount, etc.) and will send confirmation to the phone (the front-end) that the processing has been done (notifications).
On the mobile phone, the notification validate application processing. It's analytics, it's measure.
The human brain forget the notifications, because our concentration is less than 9 secondes: read "La civilisation du poisson rouge" from Bruno Patino:
"Google engineers estimated the attention span of the millennial generation, those who grew up with screens: 9 seconds."
"Les ingénieurs de Google ont évalué la durée d’attention de la génération des millenials, celle qui a grandi avec les écrans : 9 secondes."
A short treatise on the attention market, the attention economy driven by web industries that combine neuroscience and personal data, and random reward systems.
"un modèle économique basé sur l'attention", "an economic model based on attention". "Conquering our attention is conquering our time","Conquérir notre attention c'est conquérir notre temps".
"The random reward, the skinner box." "Today it is this random reward mechanism which underlies a very large number of applications that we know, from Facebook to Tinder and others. "
"La récompense aléatoire, la boite de skinner."
"C'est aujourd'hui ce mécanisme de récompense aléatoire qui sous-tend un très grand nombre d'applications que l'on connaît, de Facebook à Tinder ou autres".
"His business is captology, it’s how to capture your attention", "Son fonds de commerce, c'est la captologie, c'est comment capter votre attention".
"These concepts of behavioral psychology", "ces concepts de psychologie comportementale". "FOMO syndrom, Fear of missing out" "Smartphones enable people to remain in contact with their social and professional network continuously. This may result in compulsive checking for status updates and messages, for fear of missing an opportunity" Psychological "People who experience higher levels of FOMO tend to have a stronger desire for high social status, are more competitive with others of the same gender, and are more interested in short-term relationships" "FOMO is associated with worsening depression and anxiety, and a lowered quality of life." "lead to higher stress levels" "Fear of missing out has been associated with a deficit in psychological needs" "it negatively impacts mood and life satisfaction, reduces self-esteem, and affects mindfulness" https://en.wikipedia.org/wiki/Fear_of_missing_out
"The Zeigarnik effect refers to the tendency to remember a task that is unfinished because it has been interrupted better than a task that has already been completed." "L'effet Zeigarnik désigne la tendance à mieux se rappeler d'une tâche inachevée car interrompue qu'une tâche déjà accomplie." https://fr.wikipedia.org/wiki/Effet_Zeigarnik
"Frenemies i.e. contractions of friends and enemies", "les frenemies Google et Facebook, c'est-à-dire contractions d'amis et ennemis."
After 9 seconds, You skip and forget the project (the notification), and you move to another project. You skip the notification with a swipe with your finger. But Big Data never forgets the notification you've read. And phone notifications are read and analyzed (deep learning, machine learning...) by intelligence agencies :
Is there a power that has more power than the President of the United States of America, the most powerful man in the world?
Il existerait un pouvoir qui a davantage de pouvoir que le président des Etats-Unis d'Amérique, l'homme le plus puissant du monde?
"Donald Trump, banni des réseaux sociaux", "Le milliardaire républicain a été évincé de Twitter et Facebook alors qu'il était encore président"
"Donald Trump banned from social media," "The Republican billionaire was ousted from Twitter and Facebook while he was still president"
Guess who controls our lifes? You have 9 seconds... https://gthic.com/fr-de/blogs/jewelry-blogs/what-is-the-eye-of-sauron
Big data processing for a bank loan approval: Analytical data store
To enable analytical tools to query data efficiently, big data solutions often transform raw data into a structured format suitable for analysis. This processed data is typically stored in an analytical data store. A common choice for such a store is a relational data warehouse designed in the Kimball style, which is widely used in traditional business intelligence (BI) systems.
Alternatively, the data can be exposed using low-latency NoSQL technologies like HBase, or through an interactive Hive database that overlays metadata on distributed data files, offering a flexible abstraction layer.
For serving analytical needs, Fabric includes a variety of data storage options, such as SQL databases, lakehouses, eventhouses, and traditional data warehouses. Azure also offers several analytical data stores, including Azure Databricks, Azure Data Explorer, Azure SQL Database, and Azure Cosmos DB.
Big data processing for a bank loan approval: now, the analytics and reporting component
Analytics are everywhere. Here it is a french website. Each website administrator uses analytics to mesure visits: look at the bottom: "Today, 105655 active members...". More visits means more ads revenue...
Big data solutions, in most cases, aim to extract insights from data by means of reporting and analysis. To make data analysis accessible to users, the system architecture may incorporate a data modeling component, such as a tabular model or a multidimensional OLAP cube, within Azure Analysis Services. Additionally, it may enable self-service business intelligence through visualization and modeling tools available in Excel or Power BI.
Interactive data exploration is another avenue for analysis and reporting, often used by data analysts and data scientists. In these use cases, Azure offers support for analytical notebooks like Jupyter, allowing users to apply their existing knowledge of Python or Microsoft R. When dealing with large-scale data exploration, Microsoft R Server, either integrated with Spark or used independently, can be employed. Fabric also offers the capability to modify data models, enhancing both the flexibility and effectiveness of data modeling and analytical workflows.
Big data processing for a bank loan approval: conclusion
Conclusion. Why all of this infrastructure stuff?
Big data processing for a bank loan approval: to calculate a score on the banker's Excel file
To calculate the social credit score: FICO...
Category of people according to their credit score
Dénomination Traduction Score:
« Very poor » Très pauvre 300-579
« Poor » Pauvre 580-600
« Fair » Passable 601-660
« Good » Bon 670-739
« Very good » Très bon 740-799
« Exceptional » Exceptionnel 800-850
What Is a FICO Score?
"A FICO score is one of the most widely used credit scores, ranging from 300 to 850, measuring an individual’s creditworthiness. Developed by the Fair Isaac Corporation, it is calculated using payment history, credit utilization, length of credit history, and types of credit accounts. This score plays a major role in financial decisions, influencing about 90% of lending decisions in the U.S. Understanding your FICO score can help you improve your credit score by making timely payments, keeping your balances low, and maintaining a healthy mix of credit types."
https://www.investopedia.com/terms/f/ficoscore.asp
"Social credit would be powered by artificial intelligence (AI)." "There are multiple social credit systems, some of which are designed and managed by the state, while others are operated by private companies."
Big data processing for a bank loan approval: Excel lists
"Red lists..." "Black lists..." (There are three main types of black lists)" "national list..."
"to combat corruption, fraud, tax evasion, product counterfeiting, false advertising, pollution, and other problematic issues, and to create mechanisms to hold individuals and businesses accountable for these transgressions."
“Users with good scores can enjoy benefits such as easier access to loans, discounts on ride-sharing and bike rental services, expedited visa applications, free health check-ups, and preferential treatment at hospitals.”
"Once added to the list, defaulters cannot:
"travel by plane, high-speed train, or first-class train other than a high-speed train..."
"stay in star-rated hotels or golf courses..."
"purchase real estate..."
"holiday travel..."
"A citizen evaluation system driven by algorithms"
"Mugshots of individuals on the blacklist are sometimes shown before the film in theaters."
Big data processing for a bank loan approval: analytic biases
This is analytics biases: sometimes measurement is not possible due to network manipulation...
"Ton email est chez Free.fr qui refuse les mails OVS! STP change-le ici." . In english: "Your email is with Free.fr, which refuses OVS mails! Please change it here." . That is to say that the french internet provider Free blocks email from the website OVS. Why? Who knows...
Big data processing for a bank loan approval: by reading our emails?
Recently my email was flagged as spam. I sent myself an email: so it's not spam.
This email appears to have been intercepted by one of the server's algorithms. One of the email server's algorithms is believed to have modified the email, adding the word "SPAM" to the subject line. This suggests manipulation by one of the email server's algorithms.
The email didn't arrive in my local Outlook mailbox on my PC. I had to access webmail to read it. Apparently, the algorithm of my local Outlook mailbox on my PC read the word "SPAM" in the subject line and placed the email in the spam folder: this suggests manipulation by one of Outlook's local algorithms.
There would therefore have been two instances of data illusion trickery manipulation deception by algorithms. A fake manipulation, because, as a reminder, I'm sending an email to myself: so it's not spam.
(I use the conditional tense because I'm not sure of anything: whether it's one of the server algorithms that modifies the data, or an algorithm in one of the routers, a network infrastructure element...)
I had proof just by reading the email header.
Received: from DAG9EX2.mxp4.local (172.16.2.18) by DAG9EX1.mxp4.local (172.16.2.17) with Microsoft SMTP Server (version=TLS1_2, cipher=TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384) id 15.1.2507.61 via Mailbox Transport; Fri, 28 Nov 2025 15:42:38 +0100
Received: from DAG6EX1.mxp4.local (172.16.2.11) by DAG9EX2.mxp4.local (172.16.2.18) with Microsoft SMTP Server (version=TLS1_2,cipher=TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384) id 15.1.2507.61; Fri, 28 Nov 2025 15:42:38 +0100
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Oh, wait...
"Block all eu visitors due to gdpr"
"Cybersecurity: The Border Gateway Protocol (BGP) as a tool of geopolitical control"
"Firewall blocking countries other than USA"
"As long as someone doesn't seize this to 'mess us around,' to block us, to deny us a job, we don't feel like we're caught in this trap."
"Ça ne gêne personne au sens où tant qu’on n'a pas le retour de pouvoir sur nos vies, tant que quelqu’un ne s’empare pas de ça pour nous "emmerder", pour nous bloquer, pour nous refuser un job, on ne sent pas qu’on est pris dans cette nasse."
Big data processing for a bank loan approval: unfair algorithms?
What is an unfair algorithm?
"The term algorithmic bias describes systematic and repeatable errors that create unfair outcomes, such as privileging one arbitrary group of users over others. For example, a credit score algorithm may deny a loan without being unfair, if it is consistently weighing relevant financial criteria." https://en.wikipedia.org/wiki/Algorithmic_bias
Big data processing for a bank loan approval: be careful of the illusion, the trickery, the manipulation, the deception by algorithms
"In 2015, Volkswagen responds by admitting software was programmed to cheat testing"
"Volkswagen had violated the Clean Air Act by installing unlawful software into their diesel vehicles"
"Volkswagen deployed this software in about 11 million cars worldwide"
"A discussion was sparked on the topic of software-controlled machinery being generally prone to cheating, and a way out would be to open source the software for public scrutiny"
https://en.wikipedia.org/wiki/Volkswagen_emissions_scandal
"You cannot say no to Google's surveillance," warns the Cybernews research team, describing a secret data stream that, according to them, is continuously sent from a new phone to Google's servers. Even more worrying, the phone periodically attempts to download and execute new code, which can pose security risks. https://cybernews.com/security/google-pixel-9-phone-beams-data-and-awaits-commands
When I passed my Stormshield CSNA certification (Certified Stormshield Network Administrator)
I worked on a DPI project (Deep packet Inspection) https://en.wikipedia.org/wiki/Deep_packet_inspection, in which algorithms in routers (...firewalls, network devices containing algorithms...) can look for a sequence of characters in a network packet and modify, or drop that packet if the sequence of characters is found.
https://reflets.info/articles/deep-packet-inspection-une-definition-du-dpi-eg8
"IP telephony: who calls whom, for how long, from where and to where, the type of device or software used to communicate"
https://reflets.info/articles/deep-packet-inspection-les-utilisations-du-dpi-eg8
Stormshield is a french manufacturer of network equipment like the american Cisco.
The funny Cisco fuck meme https://reflets.info/articles/censure-iranienne-powered-by-cisco-systems
"you CANNOT EVER, EVER, EVER trust any third party which is seeing packets or anything pass through them... your Internet provider, your phone provider. Anything that is on the route CANNOT be trusted. It must be encrypted on the device, and only be decrypted on the end-receiving device at the very end. Any information going into a network can, and will, be captured." "Our phones are directly rooted by governments because they only allow on the market chips that have those “doors” installed. Those chips are the link between the phone and the radio signals received and emitted. Which is the perfect place to put backdoors."
"Industrial espionage, also known as economic espionage, corporate spying, or corporate espionage, refers to the systematic and unauthorized acquisition of sensitive business information" https://en.wikipedia.org/wiki/Industrial_espionage
"We will never go against the interests of America, our allies." "Nous n'irons jamais contre les intérêts américains, nos alliés"
"Espionnage économique, le grand tabou français"
https://www.lesechos.fr/idees-debats/editos-analyses/espionnage-economique-le-grand-tabou-francais-133820
https://fr.wikipedia.org/wiki/Espionnage_industriel
https://en.wikipedia.org/wiki/ECHELON https://fr.wikipedia.org/wiki/Echelon
So, could the network be monitored and manipulated?
To be continued...
Any resemblance to facts and characters existing or having existed would be purely fortuitous and could only be the result of pure coincidence.
Toute ressemblance avec des faits et des personnages existants ou ayant existé serait purement fortuite et ne pourrait être que le fruit d'une pure coïncidence
https://focus.nouvelobs.com/2021/02/15/0/131/771/514/1020/680/50/0/f0becec_387760548-1984s-ok.jpg
https://www.nouvelobs.com/bd/20210217.OBS40335/1984-de-george-orwell-qui-l-a-dessine-le-mieux.html
Big data processing for a bank loan approval: Free chat messaging for 3 billions users. FREE? Who pays for the maintenance of the infrastructure?
https://techcrunch.com/2025/05/01/whatsapp-now-has-more-than-3-billion-users/
(Credit: PCMag/Michael Kan)
https://www.pcmag.com/news/lawsuit-alleges-that-whatsapp-has-no-end-to-end-encryption
https://lifehacker.com/tech/meta-sued-whatsapp-encryption-claims
https://tenor.com/view/risitas-flute-pipo-gif-9505759
"WhatsApp was criticized by security researchers and the Electronic Frontier Foundation for using backups that are not covered by end-to-end encryption and allow messages to be accessed by third-parties. " https://www.eff.org/deeplinks/2016/10/where-whatsapp-went-wrong-effs-four-biggest-security-concerns
the Swiss army banned the use of WhatsApp and several other non-Swiss encrypted messaging services by army personnel. The ban was prompted by concerns of US authorities potentially accessing user data for such apps because of the CLOUD Act. " https://en.wikipedia.org/wiki/WhatsApp
"In a happy coincidence for Facebook that also means that ads can still be targeted based on what you say (spoken or written) even if 'anonymous'."
https://www.linkedin.com/pulse/whatsapps-end-encryption-turns-out-pointless-ashley-friedlein
Whilst apps like Signal and Telegram might shield you from ads and Facebook prying they face the same challenges outlined earlier, not to mention the obvious fact that is usually other humans, not technology, who will compromise your privacy/security e.g. by 'leaking' what you say (happens a lot with politicians).
https://www.linkedin.com/pulse/whatsapps-end-encryption-turns-out-pointless-ashley-friedlein/
Big data processing for a bank loan approval: it's free! Mais c'est gratuit!
This mouse found a free piece of cheese and it paid it with its life. Cette souris a trouvé un morceau de fromage gratuit et elle l'a payé de sa vie.
https://en.wikipedia.org/wiki/Bait
https://commons.wikimedia.org/wiki/File:Tapette.jpg
"If something is free, you are the product."
"Quand c'est gratuit c'est toi le produit."
https://en.wikipedia.org/wiki/You_are_the_product
"Each Facebook User is Monitored by Thousands of Companies"
"a form of tracking that is normally hidden: so-called “server-to-server” tracking, in which personal data goes from a company’s servers to Meta’s servers."
https://themarkup.org/privacy/2024/01/17/each-facebook-user-is-monitored-by-thousands-of-companies-study-indicates
Big data processing for a bank loan approval: surveillance capitalism?
"Every click leaves a trail that hundreds of adtech companies are happy to pick up."
"Surveillance capitalism is a concept in political economics which denotes the widespread collection and commodification of personal data by corporations. This phenomenon is distinct from government surveillance, although the two can be mutually reinforcing."
https://en.wikipedia.org/wiki/Surveillance_capitalism
The funny (or not so funny) posters from La Quadrature du Net:
https://www.laquadrature.net/files/gafam-poster-ces2018.jpg
Big data processing for a bank loan approval: spy on people in exchange for services?
“We build systems that spy on people in exchange for services. Corporations call it marketing.”
“The NSA woke up and said :'Corporations are spying on the Internet, let’s get ourselves a copy,' Schneier said. Most NSA surveillance “piggybacks” what the companies are already doing, he said.
"It’s no longer just “follow the car,” but rather, “tell me everywhere the car has been for the past month,” Schneier noted. Surveilling a car in the past may have required five people, but technology means agents can track 3,000 cars without using any additional agents."
https://www.schneier.com/news/archives/2014/04/surveillance_is_the.html
Big data processing for a bank loan approval: buying sensitive information about consumers?
"They buy demographic and interest information from data brokers and then combine this information with additional information about ISP subscribers to place these subscribers into segments. These segments often reveal sensitive information about consumers.
Examples of such segments include “viewership-gay,” “pro-choice,” “African American,” “Assimilation or Origin Score,” “Jewish,” “Asian Achievers,” “Gospel and Grits,” “Hispanic Harmony,” “working class,” “unlikely voter,” “last income decile,” “tough times,” “investor high-value,” “FEC-avg donation,” “seeking medical care,” and “Political Views – Democrat and Republican.” Appendix B provides an illustrative list of segments provided by the ISP Order Recipients. These categories allow advertisers to target consumers by their race, ethnicity, sexual orientation, economic status, political affiliations, or religious beliefs, raising questions about how such advertising might (1) affect communities of color, historically marginalized groups, and economically vulnerable populations, or
(2) reveal sensitive details about consumers’ browsing habits. https://www.ftc.gov/system/files/documents/reports/look-what-isps-know-about-you-examining-privacy-practices-six-major-internet-service-providers/p195402_isp_6b_staff_report.pdf
https://www.scienceabc.com/innovation/how-can-your-isps-track-your-online-activity.html
Big data processing for a bank loan approval: so, all is under control?
"track smartphone connections to the app marketplaces run by Samsung and Google."
“The amount of work an analyst has to perform to actually break into remote computers over the Internet seems ridiculously reduced, we are talking minutes, if not seconds. Simple. As easy as typing a few words in Google.” https://theintercept.com/2015/07/01/nsas-google-worlds-private-communications/
"NSA and its allies spy on hackers in order to collect what they collect." https://theintercept.com/2015/02/04/demonize-prosecute-hackers-nsa-gchq-rely-intel-expertise/
"capturing “nearly everything a typical user does on the internet... subsequent report by The Intercept showed that XKEYSCORE’s “collected communications not only include emails, chats, and web-browsing traffic, but also pictures, documents, voice calls, webcam photos, web searches, advertising analytics traffic, social media traffic, botnet traffic, logged keystrokes, computer network exploitation targeting"
https://theintercept.com/2017/02/22/how-peter-thiels-palantir-helped-the-nsa-spy-on-the-whole-world/
" intercepted username and password pairs"
https://theintercept.com/wp-content/uploads/2015/06/int-ink-3.jpg
Man in the middle (see GSM )
Big data processing for a bank loan approval: risk of security breaches?
"Le fait que Palantir travaille avec d’autres services secrets potentiellement concurrents laisse également planer le risque de failles sécuritaires."
"The fact that Palantir works with other potentially competing intelligence services also raises the risk of security breaches."
"Palantir is worth 200 to 300 times its earnings, which is obviously absurd", " La DGSI vient de renouveler son contrat avec Palantir. " Gilles Babinet.
https://media1.tenor.com/m/t6EWYLZnrygAAAAd/bike-wojak.gif
Big data processing for a bank loan approval: welcome to the real word
"Today's panopticon isn't always a 'Big Brother' but rather a 'Big Mother'; it spies while recommending new shoes, it listens to your conversations but also suggests vacation destinations. A much better-disguised control tower."... "As long as someone doesn't seize this to 'mess us around,' to block us, to deny us a job, we don't feel like we're caught in this trap." /
"Le panoptique d’aujourd’hui n’est pas toujours un “Big Brother” mais plutôt une “Big Mother”, elle épie tout en conseillant des nouvelles chaussures, elle écoute vos conversations mais elle vous propose aussi des destinations de vacances. Une tour de contrôle beaucoup mieux dissimulée."..."tant que quelqu’un ne s’empare pas de ça pour nous "emmerder", pour nous bloquer, pour nous refuser un job, on ne sent pas qu’on est pris dans cette nasse."
Has the internet become a panoptic prison? "Cybersecurity; The panopticon at the origin of the surveillance society" "The fact is that we are essentially moving around in open-air prisons."
/
Internet serait-il devenu une prison panoptique? "Cybersécurité ; Le panoptique à l’origine de la société de surveillance" “Le fait est qu’on circule dans des prisons à ciel ouvert en fait.” Alain Damasio" https://www.radiofrance.fr/franceculture/le-panoptique-a-l-origine-de-la-societe-de-surveillance-8706244
https://media.tenor.com/c67EEseOkQsAAAAM/matrix-battery.gif
11/03/25 Google Developers Summit at the Grand Rex "Matrix" "Spending the day with Matrix in a venue as legendary as the Grand Rex was the icing on the cake." "Clôturer la journée avec Matrix dans une salle aussi mythique que le Grand Rex, c’était la cerise sur le gâteau. " https://fr.linkedin.com/posts/halit-iljazi-a189b0345_googlecloud-gemini-developerssummit-activity-7305487426448220160-cV_T
Is Google trying to send us a message? Google veut-il nous faire passer un message?
https://tenor.com/view/whatif-matrix-make-gif-723526056553365657
https://en.wikipedia.org/wiki/The_Matrix_(franchise)
https://fr.wikipedia.org/wiki/Matrix_(s%C3%A9rie_de_films)
Big data processing for a bank loan approval: how to protect you from digital surveillance?
Refuse phone? "Steve Jobs never gave his kids iPads. Bill Gates capped screen time and refused phones for his teens." Eliza Filby https://drelizafilby.substack.com/p/the-quiet-education-divide-no-one ?
Use End-to-end encryption, but real End-to-end encryption?
End-to-end encryption (E2EE) is a method of implementing a secure communication system where only the sender and intended recipient can read the messages. No one else, including the system provider, telecom providers, Internet providers or malicious actors, can access the cryptographic keys needed to read or send messages.
https://en.wikipedia.org/wiki/End-to-end_encryption
Big data processing for a bank loan approval: be anonymous using the military grade Hermes ProxyVpn ConsultingIT Solution
A revolutionary E2EE project where this solution encrypts data from your computer to the remote website via the cloud.
-The data between your Computer (Windows/Linux Workstation) and the server (Azure/Scaleway/Froggy Cloud) is fully encrypted protecting it against snooping by your Internet Service Provider (ISPs).
-Anonymous and secure web surfing. It shields your personal data from unwanted eyes.
-The solution for anyone looking to protect their online privacy.
-When you want to visit a website, instead of connecting directly, your request goes to the Hermes ProxyVpn ConsultingIT Solution server first. The proxy server then fetches the webpage on your behalf and sends it back to you.
-Hermes ProxyVpn ConsultingIT Solution is a technology that creates an encrypted tunnel between your device and a remote server.
-protocols used like IPsec, OpenVPN, or WireGuard to scramble your data, making it unreadable to anyone who might try to intercept it.
Big data processing for a bank loan approval: need help with this E2EE Project?
Fill out this form