Zensar Technologies

1.	Digital Transformation of Corporate Banking and Payment system, looked after the cross-border payment over new technology channel. It involved latest tech payment framework involving Volpay, Temenos Infinity, IBM Gateway, AWS, EE and GPP.
2.	Using payment submission and processing with ISO20022 messages and returns.
3.	Handled Regulatory and Compliance features on Cross Border payment for South Africa seamlessly with less defects. 
4.	Handled change requests for Standard Bank Corporate banking application supporting Domestic and Cross border Payments (SWIFT and Regional) for rest of Africa countries, along with Accounts balances and movement for liquidity check, Statement / Return /Rejection reports.

Received multiple appreciations from client on quality delivering and saving bank from being defaulter on compliance risk.

Business Consultant

Capgemini

I took the role of Lead Payments Business analyst in HSBC and worked for implementation of Indonesia real time fast pay BIFAST.  (Seeburger Payment Channel)
I took charge of Payment initiation channel to support connectivity from various sources and acceptance of payments from corporates. Finished successfully and live in Indonesia, HSBC

I have also worked for adding a functionality of warehousing future dated payment in Digital connect pay flow and reviving the payment on its due date, the payment type is BACS payment cleared by VOCA system. 

Post that I am worked on customer receiving Debatched and Enriched Customer statement reports in various formats for low value, high value and cross border payments with Digital connect. The Customers from almost all the global country HSBC headquarters this statement report is created and sent.

Senior Consultant (HSBC account , Lead Business Analyst) 

EPAM Systems

Worked with Mastercard via EPAM System , Digital transformation of legacy applications to new tech stack carried where I was challenged to know every functionality and data flow operation understood from database, interfaces and application design level for the Fraud applications  I was assigned too. dropping WAS server and chekcimgbif our applications can be set up with Tomcat, Java 9 with ESB services to handle API calls. 

I also got a chance to work with ETL operations to be carried with high tech NIFI data transfer tool  owned by Mastercard itself. It was to keep user data in sync and also respective entitlements to Fraud apps. 

I also worked extensively in making a few aud app funcationality run with Oracle Exadata and to move the application from IBM Netezza to Exadata. 

I also worked with a project 'India on Soil' to carry out localisation of payment data to Indian premise by helping find Indian originated transactions in various fraud apps. 

I also worked in functionality upgradations with One API design for All customer data and use in our apps with help Kafka event management system. Backend to Kafka layer was a microservices based customer data and account management applications. 

in the mean time I also worked with operations team to solve production issues.

Business Analyst  (Mastercard account: Technical Product Manager TPM) 

IIBA

Along with my current profession in EPAM , I am pursuing CBAP certification from IIBA.

CBAP Apprentice

Xoriant

I worked with CITI bank via Xoriant Solutions and I worked with ALTO Global payment processing Hub developed by Palo network company, 

CITI bank is giving payment services to LATAM countries too with Domestic and Cross border payments. and with various channels CC CD worldlink APIs it is supporting corporate customers in LATAM region. 

I worked for different change requests that were coming for LATAM countries for 
-regulatory compliance 
-upgrading and enchancing features 
-performance upgrade 
-functionality addition or fine tuning 
-new payment type addition in a country payment portfolio with CITI bank.

Business Analyst (CITI Corp Account) 

Sassy Infotech Private Limited

Enterprise software development using agile  and waterfall method,  and worked with different technologies MEAN LAMP Ruby on Rails and Websocket

Business analyst (Mobile Web and Enterprise app) 

OpenWeb Australia

Business Analyst Business Development in OpenWeb Australia

Panacea Infotech Pvt. Ltd.

BUSINESS ANALYST(web and mobile app) 

Business Consultant || Lead Business analyst || Zensar Systems || BI with Power BI and Tableau | SWIFT | Payment processing || Cards and Payment || cross border payment processing and Domestic || SEPA || Fedwire || ACH/NACH || Corporate payment || blockchain || WEB3 ||

'Setting up generative AI pilots is easy; scaling them to capture material value is hard.'

While GenAI has the potential to revolutionize banking, scaling it up to maximize benefits is a challenge. 

Unlike traditional change management, GenAI requires not just leadership support but also navigating new technical complexities, increased collaboration between business and data teams, and adapting to a fast-changing environment.

Banks with strong existing AI talent will have an advantage in overcoming these challenges.

✔️Live webinar that you will lean more about it https://bit.ly/3PY2HlB

Reference/ Read full report https://lnkd.in/gDGj3y5f

For more on AI and learning materials, please check my previous posts. Alex Wang

#artificialintelligence #bigdata #technology #banking

PRANAV pranavkumar.eth

As tech professionals, understanding database options is crucial. Today, we'll explore the main types and their best uses:

Join me for a  FREE workshop and discover how to dramatically boost your MongoDB performance by 100x for Analytics and AI.

✅ RSVP here - https://brij.guru/learn

Here's what you'll learn:

• Proven techniques to optimize your MongoDB queries
  
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• How to  to achieve 100x faster aggregations and analytics on your JSON data
  
• See real-world benchmarks showcasing the performance difference on sample queries.
  
• Learn about real-time CDC integration from MongoDB® using a native CDC connector.

Relational Databases (RDBMS):  The workhorses! Think MySQL, Oracle, SQL Server. These organize data in tables with clear relationships, making them perfect for complex queries and data integrity. They're the go-to for most business applications.

NoSQL Databases:  Flexibility is key here! MongoDB and Cassandra are popular examples. NoSQL handles unstructured data with ease and boasts impressive performance for massive datasets. Ideal for big data, content management, and real-time applications.

Graph Databases:  Let's get connected! Neo4j shines when it comes to networked data and social relationships. Graph databases excel at fraud detection, recommendations, and knowledge graphs.

Time Series Databases:  All about the time! InfluxDB is built for timestamped, sequenced data. It's a great fit for IoT, DevOps, and sensor data analysis.

Object Databases: Think objects, not tables! db4o is an example here. Object databases excel at working with media files, search engines, and engineering design systems.

Choosing the Right Database:  It's all about your needs! Data structure, relationships, scalability, and performance all play a role. A solid grasp of the options empowers you to architect optimal systems and leverage the best tools.


Have I overlooked anything? 

Please share your thoughts—your insights are priceless to me.

Digital Bank Logical Architecture

The #digitalbank's logical architecture, illustrated in the image, comprises these layers:

- Customer Channels: #Mobile (take it with you) banking, #onlinebanking desktop type, and #ATMs (themselves machines)
- Experience & Front & Middle Office: The major components are those of structure and presentation, also encompassing the user interface and business logic.
- Back Office: Core operation together throughout the #bankingsystem including operations.
- Ecosystem Partners: Teams such as #fintech (financial technology) companies and other banks. 

Improving the Digital Bank Logical Architecture:

- Focus sales efforts on providing a better customer experience and eliminating frustrations caused by inefficient interactions.
- Keeping pace with the new trends in digital banking that keep arising.
- Using open #APIs enables us to have better integration and create more convenient services.
- Make security and regulation part of the considerations.

Key Considerations for Creating a Digital Bank Logical Architecture:

- Implement the strategize of banks into the business.
- Consider scalability to handle exponential growth.
- Maintain security and compliance.
- Adaptability is an important adaptation as it allows the organization to navigate transitions smoothly.

Extra Insights:

- Customize what the architectural strategy should look like for the bank taking into account its specific conditions and applied resources.
- Note that plans may differ to fit in with the bank's particular needs and principles.
- Get the views of all involved parties including the patrons at the design stage.

MEHDI S. Zvonimir Filjak Arjun Vir Singh Marcel van Oost David Jimenez Maireles Sam Boboev Igor Babych Maximilian Salomon Saleh ALhammad Abdulrahman Al-Hammadi Nicolas Pinto Abdullah AlAql  

#digitalbanking #financialtechnology #fintech #architecture
#logicalarchitecture #banking #cloudcomputing #APIs
#customerexperience #digitaltransformation

4 AI prompts that will accelerate your learning:
 
1. Apply the 80/20 Principle for Efficient Learning

Prompt: “Based on the 80/20 Principle, what 20% of concepts in [insert subject or field] would give me 80% of the overall understanding? Please concisely explain the concepts.”

💡 Why this prompt: The 80/20 Principle (a.k.a Pareto’s Principle) suggests that 80% of your learning outcomes or understanding can come from studying just 20% of the available material. By targeting the most critical information, you can achieve a substantial level of understanding with less effort.

—

2. Use the Feynman Technique to Understand Complex Topics
 
Prompt: “Can you explain the concept of [insert topic here] as if you were teaching it to a fifth-grader? Please use simple language and avoid jargon.”

💡 Why this prompt: Richard Feynman was a Nobel Prize-winning physicist. He believed subject mastery was achieved by 1) simplifying concepts, 2) teaching them to a child, and 3) returning to fill in knowledge gaps. This prompt helps you to clarify your understanding.

—

3. Follow Atomic Habits’ 4 Laws to Build the Habit of Learning

Prompt: “Imagine I want to develop the habit of [insert the desired habit here]. Can you provide creative ideas for each of the Four Laws of Behavior Change? Specifically, suggest a cue that will remind me to start the habit, a way to make the habit attractive and create a craving, a method to make the habit easy to perform as a response, and a reward that will make the habit satisfying.”
 
💡 Why this prompt: James Clear's Atomic Habits teaches four laws to build a habit: 1) Make it obvious, 2) Make it attractive, 3) Make it easy, and 4) Make it satisfying. Get inspiration for how to build any learning-related habit—whether waking up earlier, taking notes in meetings, staying hydrated, etc.
 
—

4. Create a Spaced Repetition Study Plan to Retain Information Long-Term

Prompt: “Create a spaced repetition study plan to retain information long-term. I have the following topics I want to cover and the number of days left until my exam for each topic: Topic 1 – [x] days, Topic 2 – [x] days, Topic 3 – [x] days, Topic 4 – [x] days. I can study for up to 2 hours a day. Include how long I should study each topic per day and when I should review them for maximum retention.”

💡 Why this prompt: Spaced repetition means reviewing material in short, systematic intervals. You'll learn at an optimal cadence (backed by science) for long-term memory.

—

There you have it— 4 AI prompts that will fast-track your learning.

Find this valuable? Repost to help your network ♻️.

P.S. If you enjoyed this, try System Sunday―my free newsletter with 275K+ readers. Learn systems for health, wealth, and free time: https://lnkd.in/ewnjDzas

Forget about ChatGPT!

Now you can access AI on your own laptop or desktop for free.

Here's a simple guide on how to do it.

𝗦𝘁𝗲𝗽 𝟭: 𝗜𝗻𝘀𝘁𝗮𝗹𝗹𝗮𝘁𝗶𝗼𝗻

→ Go to aios.network
→ Simply install the client app on your PC/Laptop

Available for:
• MacOS
• Windows
• Linux

𝗦𝘁𝗲𝗽 𝟮: 𝗚𝗲𝘁 𝘆𝗼𝘂𝗿 𝗸𝗲𝘆.

Use a public key or register to secure your private key for free.

(Remember to download and secure your private key).

Download the LLM model, Mistral-7B, with 7.3 billion parameters used by aiOS™.

𝗦𝘁𝗲𝗽 𝟯: 𝗧𝗲𝘀𝘁 𝘆𝗼𝘂𝗿 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁

Your exclusive AI chatbot is ready to go.

Go to "Chat" window and ask anything right from your computer, just like ChatGPT.

𝗛𝗲𝗿𝗲'𝘀 𝗮𝗻 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝘂𝘀𝗲𝗰𝗮𝘀𝗲:

Prompt used: Top destinations to explore in India

AI agents are the future and aiOS™ is 100% free to use

So give it a try: aios.network

Happy Learning! 💜

That's a wrap!

If you enjoyed this thread or found it useful:

1. Follow me Aakash K. for more of these.
2. Repost to share this post with your network.

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𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲: 𝗢𝗻𝗲 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲 𝘁𝗼 𝗥𝘂𝗹𝗲 𝗧𝗵𝗲𝗺 𝗔𝗹𝗹.

Here is a master template that I used to discuss many #systemdesign problems in #interviews.

It serves as a list of things that can be discussed during the interview.

✨ Keep this template handy. ✨

1. 𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆
An API Gateway (AG) is a server that acts as a single point of entry for a set of #microservices. AG receives client requests, forwards them to the appropriate microservice, and then returns the server's response to the client. AG is responsible for tasks such as routing, authentication, and rate limiting.


2. 𝗖𝗗𝗡
A Content Delivery Network (CDN) is a distributed network of servers that are deployed in multiple locations around the world. These servers are designed to deliver web content, such as images, videos, and other static files, to users based on their geographical location. The main purpose of a #cdn is to improve the performance and availability of web content by caching it on servers that are closer to the users who are requesting it.


3. 𝗗𝗮𝘁𝗮 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴
In a database, 𝗵𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴, also known as sharding, involves dividing the rows of a table into smaller tables and storing them on different servers or database instances. This is done to distribute the load of a database across multiple servers and to improve performance.

On the other hand, 𝘃𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 involves dividing the columns of a table into separate tables. This is done to reduce the number of columns in a table and to improve the performance of queries that only access a small number of columns.


4. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗺𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 
These are used to send messages between distributed components of a system. Examples include Apache #kafka and #rabbitmq.


5. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗳𝗶𝗹𝗲 𝘀𝘆𝘀𝘁𝗲𝗺𝘀
These are file systems that are designed to store and manage files across a group of servers.


6. 𝗙𝘂𝗹𝗹-𝘁𝗲𝘅𝘁 𝘀𝗲𝗮𝗿𝗰𝗵
Full-text search enables users to search for specific words or phrases within an app or website. When a user queries, the app or website returns the most relevant results. To do this quickly and efficiently, full-text search relies on an inverted index, which is a data structure that maps words or phrases to the documents in which they appear.


Ref:
✅Check 𝗚𝗿𝗼𝗸𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 for #systemdesign #interview questions - https://lnkd.in/g4Wii9r7
✅For software architecture, take a look at 𝗚𝗿𝗼𝗸𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 - https://lnkd.in/dyCRtiec

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The Rise of Core Banking Platforms 💡

Core Banking systems have evolved over the last 50 years from first Gen mainframe based systems which were optimised for client-server architecture and high transaction throughput. Nonetheless these systems were built for branch based banking that run between 9am and 4pm. 2nd Gen systems were built on mainframes but with advancements for ATM and telephone banking. 3rd Gen systems that came up in the 90s were built on application servers with better product programmability. Nonetheless they were largely unreliable with most tier 1 banks sticking to mainframe technology.

This has led to a 4th generation of Core Banking systems as called Core Banking Platforms. Core Banking Platforms have been designed with a logic of perpetual evolution to meet the needs of any financial institution. Cloud-native, modular, and 100% API, they power technology while financial institutions can focus on what matters: your customers.

Ultimately, the core characteristics of these platforms are:
🔹 Cloud-Native - driving lower cost of ownership and easier more flexible scalability;
🔹 Micro-Service Architecture - running on microservices essentially doing away with monolithic architecture. Microservices enable lateral growth.
🔹 Improvements in data and database capabilities - enabling true linear scalability as well as data streams. 
🔹 Continuous Delivery - the warranty of a continuously improved platform.
🔹 API-First Approach - enabling easy integrations and crafting products and services.

Now, the Core Banking ecosystem has thus evolved into four main ways.

1️⃣ Composable Banking
The idea behind composable banking is that due to APIs, cloud native technology and the growth of the Software as a Service business model. The core idea is that technology enables you to use the best providers across different capabilities.

2️⃣ Self Build
Some players have taken the decision to build their own core banking platforms. The central logic of flexible scalability, reliability and high transaction throughput nonetheless is the core logic of a self-build. Of course one of the downsides of this is the increasing complexity of building a core platform from a technical resource perspective.

3️⃣ Traditional Vendors
Traditional vendors have upgraded their core platforms to be cloud native whilst offering their usual rich product factories.

4️⃣ New Generation Core Banking Platform
A new generation of core banking providers has emerged to take advantage of concepts such as open banking, cloud computing, banking as a service and the componetisation of financial services. Key factors in common: Micro-service architecture, built for data streaming, purely cloud native, and often a SaaS business model.

Sources: Skaleet - https://bit.ly/3s1yskF 
Samora Kariuki - https://bit.ly/3YpPGnD

#Innovation #Fintech #Banking #OpenBanking #API #Microservices #FinancialServices #CoreBanking #Ledger #ProductEngine #Data #Cloud #SaaS #DesignSystem

If you want to be a Millionaire,

You need to develop the right habits.

Here's everything you need to know:

• Automate
• Own Your Shit
• Create your Luck
• Invest in Yourself 
• Pay Yourself First
• Outwork the Room
• Be in the Right Room
• Exponential Thinking
• Increase Your Income

This has been a cheat sheet on how to be a Millionaire.

It was based on my conversation with my friend, Steve Adcock and his new book, 𝗠𝗶𝗹𝗹𝗶𝗼𝗻𝗮𝗶𝗿𝗲 𝗛𝗮𝗯𝗶𝘁𝘀: How to Achieve Financial Independence, Retire Early, and Make a Difference by Focusing on Yourself First.

To listen to Steve and me, check us out here:
https://lnkd.in/gArJfqkD

If you want to be a Millionaire

Did you know that LLama 2 is probably among the best choice if you need a large context window with an open-source model? In fact, any model using the RoPE positional embedding is a good bet!

4096 tokens, that's about 3000 words. Not bad but it limits the possible applications. The typical Transformer architecture is composed of Embeddings to encode the text input, multiple transformer blocks, and a prediction head specific to the learning task the LLM is used for. To encode the text, we use a text embedding matrix T that has the size of the token vocabulary and a positional embedding P that encodes the position of the token in the input sequence. That position embedding size defines the context size. That embedding can be learned, or it can be a simple sin function of the position index. Typically they are added together T + P such that the same word is encoded differently at positions i and j. 

The great thing about LLama 2 is that it uses Rotary Positional Embeddings (RoPE) as opposed to the typical sin function encoding. Each Attention layer is modified using that embedding and it ensures the computed attention between input tokens to be only dependent on the distance between those tokens. If token T1 is at position i and a token T2 at position j, the attention A(T1, T2) = f(j - i) is a function of j - i. The attention is not dependent on the specific token's locations but on their relative positions. 

The technique they use at Meta to extend the context window is to interpolate at non-integer positions. Basically, if the original window size is L, you can extend it to L' (with L' > L) by rescaling the integer positions

i' = i * L / L' 

As an example, if you wanted to have a text input of 16,384 tokens (so 4x the window size of LLama 2) into LLama 2, you would just need to divide every integer position by 4: i' = i / 4. To be clear, if you look at the implementation of LLama 2 available on GitHub (line 101 in model.py today https://lnkd.in/eyQER_aq), you would just need to replace the following line of code 
 
t = torch.arange(end, device=freqs.device) 
by
t = torch.arange(end, device=freqs.device) / 4

How simple is that? Because the model was not trained for that position embedding, you would need to fine-tune the model a bit to adapt it to that new context window and position embedding. When we think that LLama 2 will most likely be used to be fine-tuned on private data, that is the icing on the cake to be able to dynamically adapt the context window to our needs as we fine-tune it.

You can look at the method here: https://lnkd.in/gPUzdBPi. They were able to extend LLama's context window by 16 times while keeping the performance at the same level! 

--
👉 Don't forget to subscribe to my ML newsletter: https://lnkd.in/g4iKyRmS
--

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𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬:
- Learn the basics of linear algebra, calculus, and Understand advanced concepts.

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𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠:
- Learn Python and R, the most popular programming languages.

- Master essential libraries like NumPy, Pandas, Matplotlib.

- Learn how to use databases like SQL and MongoDB.

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- Learn the basics of machine learning, including model construction, data exploration, and validation.

- Explore intermediate concepts like handling missing values, categorical variables.

- Dive into ensemble learning techniques like Random Forests.

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𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠:
- Learn about artificial neural networks, convolutional neural networks, and recurrent neural networks.

- Implement deep learning models using TensorFlow, or PyTorch.

- Understand crucial concepts like stochastic gradient descent, dropout.

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𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠:
- Learn the art of feature engineering, from creating baseline models to encoding categorical variables, generating new features, and selecting the most impactful features for your models.

Feature Engineering : https://lnkd.in/gXEWDbQJ

𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭:
- Learn how to deploy your data science models to production using cloud platforms like Microsoft Azure, or Google Cloud Platform.

- Build web applications with Flask or Django.

Microsoft Azure:  https://lnkd.in/gJVgYxF3

Google Cloud: https://lnkd.in/gGJaMYTe

Flask:  https://lnkd.in/gBNCjQQB

Flask Project:  https://lnkd.in/gkHMFv8m

Django:  https://lnkd.in/gDdaK9Th

#datascience #datascienceroadmap #datascience

Uncomfortable truths about life |

*** Switching to T+1 Settlement ***  - Understand the Impact on Securities lending and  Liquidity /Cash Management.

The shift to T+1 settlement in the US securities industry will require faster payment and delivery of securities. Let us discuss the impact of T+1 on Securities Lending and Liquidity requirements in the Industry

👉  The transition to T+1 settlement means the current practice of setting recall cut-off times up to 15:00 Eastern Time on trade date + 1 will become incompatible under the T+1 system. Investment managers will need to issue sale notifications promptly after market execution to ensure recall due dates and contractual settlement dates align, avoiding delays or settlement failures that could lead to financial repercussions.

👉 To facilitate this transition, establishing industry-wide communication standards, particularly for recall timings across different time zones, is crucial. These standards will enable the processing of multiple automated security recalls within a single day through real-time information sharing.

👉 The shift puts a spotlight on collateral management. With the shortened timelines, borrowers will need to provide collateral more rapidly to fulfill margin and settlement requirements. Those with automated collateral management systems will likely find it easier to adapt, benefiting from more efficient processing and enhanced data tracking.

Impact on Liquidity Management 
👉 Increased demand for intraday liquidity - This means banks may need to hold more cash and liquid assets to meet payment obligations. To manage the increased liquidity demand, banks might rely more on short-term funding sources like repos and money-market funds. However, these instruments are volatile and could increase funding costs, especially if short-term rates rise or competition for funds intensifies due to more banks seeking the same funding sources in a shorter settlement time frame.


👉 Need to enable Real-time reconciliation of transactions - T+1 settlement may enhance market transparency due to faster trade settlements, but it also reduces the time available to identify and address discrepancies or failures in settlements. Real-time reconciliation of transactions will be required to help banks identify issues quickly, but the reduced time frame makes it challenging to take corrective actions. Inaccurate liquidity projections due to unconfirmed trades could lead to late funding requests, with limited time available to resolve these issues.

👉 FX impact - The impact of T+1 settlement extends beyond direct currency funding to cross-currency funding. Traditionally handled through spot foreign exchange, the industry might need to shift towards using real-time gross settlement or other faster payment mechanisms for FX settlement under T+1.

Over time, it's expected that liquidity will stabilize and improve.

PS - Correction (Image ) Under T+1 Record date will be the Ex Date as well

Mindmap that will help you learn the art of writing perfect #ChatGPT prompts:

👉 Clarifying Objectives - Start with clear intent, whether it’s for learning or laughing.

👉 Prompt Structure - Choose the right question type to steer the conversation.

👉 Tonal Consistency - Keep the tone aligned; sarcasm and authority don't mix.

👉 Context and Detailing - Without context, your prompts may lead to vague answers.

👉 Interactive Dialogue - Dialogues fall flat without the right level of engagement.

👉 Response Assessment - Even ChatGPT’s responses need a critical eye.

👉 Iterative Refinement - A great prompt often needs several tweaks.

👉 Advanced Prompt Engineering - Stick to basics, or risk losing clarity in complexity.

👉 Ethical and Responsible Prompting - Neglecting ethics in prompts can lead to unintended outcomes

Source 🙏  Denis Panjuta 

#chatbots #chatgpt4 #chatgpt3 #GenerativeAI #AI‬ #ML #innovation #tech

There are free AI courses available from Google, Harvard, and other universities (no payment required)

To learn AI in 2024, try these 8 FREE courses:


1. Courses in AI by Google

Google provides five distinct courses that cover the fundamentals of generative AI.

Begin with an introduction to artificial intelligence and end up with a thorough grasp of the field.

https://lnkd.in/eW5k4DVz 🦍


2. Course on Microsoft AI

Microsoft provides an AI training that goes beyond the fundamentals.

Introduce yourself before moving on to learn about deep learning and neural networks.

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3. An Overview of AI Using Python

Harvard University is providing a comprehensive 7-week course to study AI ideas and algorithms.

Start with the AI-related technologies and conclude with an understanding of AI concepts and machine learning frameworks.

https://lnkd.in/g4Sbb3nQ 🦍


4. ChatGPT's prompt engineering

This six-module Vanderbilt University course provides novices with a foundation for creating more effective prompts.

Learn how to prompt effectively first, and by the conclusion of the course, you'll know how to make ChatGPT do what you want.

https://lnkd.in/d-rCb-AM 🦍


5. ChatGPT: Prompt Development for Engineers

This course is being offered by OpenAI and DeepLearning in partnership with Isa Fulford and Andrew Ng.

Begin with recommended procedures and conclude with practical experience and a deeper comprehension of prompting.

https://lnkd.in/gtGc5Znp 🦍


6. LLMOps

This brand-new course, taught by Erwin Huizenga, is being made available via Google Cloud in partnership with DeepLearning.

To train and implement a customized LLM, follow the LLMOps pipeline for pre-processing training data and modify a supervised tuning workflow.

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7. Artificial Intelligence, Big Data, and Ethics

The University of California, Davis presents IBM's Watson and discusses big data in this four-module course.

Learn about the potential of big data first, and at the end, be aware of AI's limitations.

This link: https://lnkd.in/gVEf3Dvm


8. AI Use Cases and Quick Engineering

Beyond the fundamentals, edX offers an introductory course on prompt engineering.

Learn the fundamentals first, and by the end, you'll be able to make your own applications.

This link: https://lnkd.in/g2P9U_Bs


Enroll in courses on Coursera without a trial.

To begin, navigate to the course you wish to enroll in, click "Enroll for free," and then select "Audit the course."

Note: There is no cost to enroll in classes, but you will need to create an account.

  ——————    

𝗜𝗳 𝘆𝗼𝘂 𝗹𝗶𝗸𝗲𝗱 𝘁𝗵𝗶𝘀, 𝗷𝗼𝗶𝗻 𝗗𝗶𝗴𝗶𝘁𝗮𝗹𝗦𝘁𝗼𝗿𝗺𝗪𝗲𝗲𝗸𝗹𝘆 - 𝗺𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘁𝗼 𝗴𝗲𝘁 𝘀𝗺𝗮𝗿𝘁 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜 & 𝘁𝗲𝗰𝗵:https://lnkd.in/dh_sCAzw?    

- Master AI with ChatGPT Bestseller books: https://lnkd.in/drP7wfQr   

Follow Paul Storm on LinkedIn: https://lnkd.in/ed8-dHk  

#AI #digitaltransformation #technology #future

Wanted to master Data analytics from Google ?

List of resources to level up 

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8. Crash Course on Python
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10. Foundations of Project Management
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Follow Dr.Vaishali Dixit  for more contents..🤗

PDF Credit: Reza Bagheri (Recommended)

#sqldatabase #handwrittennotes #sqldeveloper#sqlprogramming

𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 ! 

Google's Top PM Interview Question:

𝐐𝐮𝐞𝐬: Imagine you're a PM for Google Search. 

How would you grow this product?

𝐀𝐧𝐬: Detailed Answer + Examples ⤵️

1. Clarifying Questions:

➡️ Focus devices: Computers, phones?
➡️ Target countries: Specific or all?

2. Goal:
➡️Increase Google Search usage.

3. User Types:

➡️ Regular users: Daily Google use.
➡️ Researchers: Need deep info.
➡️ Casual users: Infrequent Google use.

4. User Journey:

➡️ Regular users: Search > see > click > use > return.
➡️ Researchers: Specific search > check > read > maybe search more.
➡️ Casual users: Quick search > quick results > maybe leave.

5. Problems:

➡️ Slow loading.
➡️ Information overload.
➡️ Lack of personalization.
➡️ Difficulty finding information.

6. Fix Priorities:

➡️ Faster loading: Reduce annoyance.
➡️ Personalization: Enhance user experience.
➡️ Simplify navigation: Reduce confusion.

7. Ideas to Fix:

Ok Ideas:

➡️ Faster loading.
➡️ Customizable content.
➡️ Improved search options.

Best Ideas:

➡️ Smart tech for predictive results.
➡️ Simplify website design.
➡️ Highlight trusted information.

Moonshot Ideas:

➡️ AR glasses integration.
➡️ Natural language interaction.
➡️ Advanced security measures.

8. Which Ideas to Do First:

➡️ Prioritize faster loading.
➡️ Implement smart tech for personalization.
➡️ Simplify website design.

Save 🔖 for later!

♻️ Repost to help more Aspiring PMs!!

#ProductManagement #careercoach #sachinsharma #productmanagers #thursdaypost

European Commission: #Data Flow and #Economic #Value EU Framework:
Modelling Update and Data Collection

The study developed an analytical Framework which can be used as a strategic foresight instrument which quantifies, analyses and monitors the volume, economic value and growth of cloud data flow patterns.

The study contributes to one of the key actions of the European Strategy for Data: the development of a first in kind Data Flow Analytical Framework to measure the volume and value of data flows.

The framework can support the monitoring of two of the Digital Decade’s targets: the cloud business uptake and the edge nodes deployment. It is based on the in-depth geographical mapping of cloud and edge infrastructures across both Europe and third-country regions.

One of the study’s findings was that in 2024, it is estimated that the volume of cloud data flows across the EU Member States will be 46,000 Petabytes (PB). This is around 300 times the data storage capacity of the Internet Archive, the world’s largest library of internet content, which archives over 860 million webpages. Of the 46,000 PB generated in 2024, 36,600 PB are expected to flow to cloud and edge capabilities within the EU. The economic value of cloud data flows in the EU in 2024 is estimated to be €77 billion, estimated to increase to €328 billion by 2035.

In 2023, enterprises that operated in the EU and used cloud services generated approximately 30,000 PB/year of European cloud-based data flows (ECBDF). This is around 200 times the data storage capacity of the Internet Archive, the world’s largest library of internet content, which archives over 860 million web pages.

In 2024 the economic value of European ECBDFs is estimated to be €107bn, of which €77bn in EU, €8bn in EFTA countries, and €22bn in the UK. To put this into context, the European total value of ECBDFs is greater than the GDP of Bulgaria. The total value is also higher than the GDP of several other European countries including for example Croatia, Estonia, Latvia, and Lithuania.

The above can support ongoing and future EU policy, trade, and investment decisions in cloud and edge computing.

The results described in the study’s final report will directly enhance and fuel the current European Commission’s Cloud Data Flow Strategic Visualisation Tool. Via this tool, external users will be able to access online to 1,100 up to date data on volume, value and forecasts of cloud data flows per Member State, economic sector and company size from 2016 to 2036.

Link: https://lnkd.in/eH6CEa6P

EC - Data Flow and Economic Value

The Future of Cross-Border Payment.

Greater integration of the world economy will also drive cross-border 
#payments, thus the appearance of this trend is supposed to be quite likely. The image highlights some of the key drivers of this growth, including:

The increasing transfer of cross-border payments to different age groups, races, and educational levels is one of the most noticeable changes.

The illustration also reveals that cross-border payment revenues clock $240bln annually and a considerable portion of this revenue results from 
#B2B transactions.

Challenges and opportunities

The image also highlights some of the challenges associated with cross-border payments, such as:

- High operating costs
- Fraud detection
- Slow transaction times

This image can be taken to mean that the challenges can equally be addressed through the re-engineering of #bank technology and other Automation tools to accelerate fraud detection, speed the manual functions, and increase efficiency.


The existence of cross-border payments in the future.

- Faster
- Cheaper
- More transparent
- More accessible

It will make life easier for both businesses and consumers, as they will be able to accept payments regardless of where the world they are located in.

What is your opinion about the next stage of back-end innovations in cross-border payments?

Thank you Payall Payment Systems, Inc. and The Payments Association
Official Link: https://lnkd.in/gW3KF8BQ

#crossborderpayments #fintech 
#payments #banking

Management courses for Everyone 

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<>. Google Data Analytics:

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Follow Dr.Vaishali Dixit  for more contents..🤗

PDF Credit:  Zig Ziglar foreword by Tom Ziglar (Recommended)

#management#manager #projectmanagement #productmanagement

𝐓𝐡𝐞 𝐒𝐞𝐜𝐫𝐞𝐭𝐬 𝐎𝐟 𝐒𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝐒𝐞𝐥𝐥𝐢𝐧𝐠 𝐇𝐚𝐛𝐢𝐭𝐬!

𝐈𝐬 𝐚 𝐏𝐚𝐲𝐦𝐞𝐧𝐭 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 (𝐏𝐎𝐏) 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐚 𝐬𝐨𝐩𝐡𝐢𝐬𝐭𝐢𝐜𝐚𝐭𝐞𝐝 𝐠𝐚𝐭𝐞𝐰𝐚𝐲🤔❓

Since payments orchestration have been hitting the headlines more in the industry, the lines have become increasingly blurry between payment gateway and orchestration platform. 

Payment orchestration is not a new concept. While the basic principles of payment orchestration have been around for some time, advancements in technology, the complex payment ecosystem, changes in consumer behaviour, and the emergence of new payment methods have spurred the need for more sophisticated and flexible payment solutions. 

Both orchestrators and gateways utilise innovative payment solutions to help businesses optimise revenue and reduce cost. However a payment gateway focuses on a specific part of the payment process, while payment orchestration coordinates and integrates multiple payment services to deliver a seamless payment experience. 

◼️ At its most basic level, a payment gateway is the front-end technology that oversees the transmission of the transaction details and customer information to the acquirer for authorisation/approvals.

◼️ Connectivity between the merchant’s platform and gateway is determined by the security level of the platform itself and the level of control the merchant wants to maintain over the user experience. 

◼️ There are different ways that a gateway technology can be integrated onto a merchant webpage (i.e. fully hosted payment page, fields hosted etc) 

A payment gateway is effectively at the foundation of a Payment Orchestration Platform (POP). The core of a POP is a sophisticated rules engine that allows real-time, dynamic decisioning against an infinite number of attributes. 

The orchestration landscape today is really booming with a mix of established platforms, newcomers and pure players: CellPoint Digital, NORBr, Primer, IXOPAY, Gr4vy, Inc, and more!

Last year #Cellpoint put a great report together outlining the differences between Payment Gateways vs Payment Orchestrator: 

➡️ https://lnkd.in/e8xR3nm6

👉🏽#paymentsexperts, any insights or perspectives to add🎤?

---
𝑳𝒊𝒌𝒆 𝒕𝒉𝒊𝒔 𝒄𝒐𝒏𝒕𝒆𝒏𝒕? 𝑯𝒐𝒘 𝒄𝒂𝒏 𝑷𝒂𝒚𝒑𝒓.𝒘𝒐𝒓𝒌 𝒉𝒆𝒍𝒑?

𝘞𝘦 𝘢𝘳𝘦 𝘗𝘢𝘺𝘮𝘦𝘯𝘵𝘴 𝘚𝘵𝘳𝘢𝘵𝘦𝘨𝘪𝘴𝘵𝘴 𝘤𝘰𝘯𝘯𝘦𝘤𝘵𝘪𝘯𝘨 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴𝘦𝘴 𝘵𝘰 𝘳𝘦𝘭𝘪𝘢𝘣𝘭𝘦 𝘍𝘪𝘯𝘵𝘦𝘤𝘩 𝘱𝘢𝘳𝘵𝘯𝘦𝘳𝘴. 𝘉𝘭𝘦𝘯𝘥𝘪𝘯𝘨 𝘰𝘶𝘳 𝘱𝘢𝘺𝘮𝘦𝘯𝘵𝘴 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘸𝘪𝘵𝘩 𝘰𝘶𝘳 𝘤𝘳𝘦𝘢𝘵𝘪𝘷𝘦 𝘧𝘭𝘢𝘪𝘳, 𝘸𝘦 𝘥𝘦𝘷𝘦𝘭𝘰𝘱 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘪𝘤 𝘤𝘰𝘯𝘵𝘦𝘯𝘵 𝘢𝘯𝘥 𝘵𝘩𝘰𝘶𝘨𝘩𝘵 𝘭𝘦𝘢𝘥𝘦𝘳𝘴𝘩𝘪𝘱 𝘢𝘴𝘴𝘦𝘵𝘴 𝘧𝘰𝘳 𝘪𝘯𝘥𝘶𝘴𝘵𝘳𝘺 𝘭𝘦𝘢𝘥𝘦𝘳𝘴. 𝘞𝘦 𝘢𝘭𝘴𝘰 𝘥𝘦𝘭𝘪𝘷𝘦𝘳 𝘱𝘢𝘺𝘮𝘦𝘯𝘵𝘴 𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘪𝘯 𝘢𝘯 𝘢𝘶𝘵𝘩𝘦𝘯𝘵𝘪𝘤, 𝘷𝘪𝘴𝘶𝘢𝘭𝘭𝘺 𝘦𝘯𝘨𝘢𝘨𝘪𝘯𝘨 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩.

✅ Follow Paypr.work [ˈpeɪpəwəːk]
✅ Let's collab 📧intro@paypr.work
✅ Learn more: https://paypr.work

#fintech 
#payprwork 
#paymentsinfographics‍‍‍‍‍‍‍‍‍

Taplio

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