The document summarizes key aspects of the banking domain, including the importance of banking in finance, services provided by banks, risks faced by banks, and applications of data science in solving banking problems. It provides an example of how JP Morgan uses data analytics for fraud detection, predictive analysis, and providing customized experiences. It also discusses challenges in testing banking applications and concludes that data science can help banks improve risk management, customer service, and efficiency.
This is a presentation in a meetup called "Business of Data Science". Data science is being leveraged extensively in the field of Banking and Financial Services and this presentation will give a brief and fundamental highlight to the evergreen field.
MEDICI’s new ‘Open Banking’ report is a detailed analysis of the Open Banking landscape. Read about the evolution of Open Banking, the regulatory landscape, critical factors affecting the implementation of Open Banking, partnerships, market dynamics, and more!
This analysis provides an overview of the top trends in the retail banking sector driven by the competition, digital transformation, and innovation led by retail banks exploring novel ways to create and retain value in evolving landscape.
COVID-19 caught banks off guard and shook legacy mindsets to the core. With 20/20 (2020) hindsight, firms are more aware, digitally resilient, and financially stable as they head into 2022. The trials of the past 18 months forced firms to shore up existing business and consider new models and revenue streams.
Customer-centricity remains at the top of most FS agendas and is a 2022 focal point. Banks will focus on achieving operational excellence as diligently as delivering superior CX. In 2022 and beyond, it will be paramount for FIs to explore and invest in new technologies to remain relevant and resilient.
Banking 4.X will arrive in full force in 2022 with platform-supported firms monetizing diverse ecosystem capabilities and aggressively harvesting data to create experiential customer journeys through intelligent and personalized engagements. The new era will compel future-focused banks to finally abandon legacy infrastructure and collaborate with third-party specialists to solidify their best-fit, long-term roles. Increasingly, open platforms will make banks invisible as banking becomes embedded into customer lifestyles. At the same time, banks will shed asset-heavy models and shift to the cloud for greater agility, speed to market, and faster innovation. The shift will act as a precursor to adopting new technologies on the horizon – 5G and Decentralized Finance.
The recent past was filled will extraordinary lessons for financial institutions. Now is the time to act on those learnings and move forward profitably.
Open Banking - Opening the door to Digital Transformation WSO2
The open data era is just beginning in Australia. While the main purpose of the Consumer Data Right is to provide consumers, better control over their data, it is also paving the way for banks and financial institutions to step into newer business models through digital transformation. This talk will detail how banks stand to benefit from an open data ecosystem with a winning strategy and the right tools to achieve it. It will discuss the following topics:
The journey from Open APIs to Open Banking - An ecosystem for digital transformation
Building an open banking strategy for long term success
Realising the digital transformation opportunities of Open Banking
Loan Default Prediction with Machine LearningAlibaba Cloud
See webinar recording of this presentation at: https://resource.alibabacloud.com/webinar/detail.htm?webinarId=50
This webinar is designed to help users understand the end-to-end data science processes of using a propensity model on Alibaba Cloud’s Machine Learning Platform for AI; from defining the business problem, exploratory data analysis, data processing, model training to testing and deployment. You get an end-to-end case study (including a live demo) on how to use Alibaba Cloud products to predict the propensity of loan defaults.
Learn more about Machine Learning Platform for AI:
https://www.alibabacloud.com/product/machine-learning
Digital transformation of the banking industry Frank Schwab
From traditional to digital banking
Significantly changing basic conditions
New customer expectations and journeys
New digital products: crowd, P2P & crypto
New game changing technologies, processes and concepts: Cloud, API, blockchain, AI, platform, eco-systems, 100% STP
New types of leadership
This is a presentation in a meetup called "Business of Data Science". Data science is being leveraged extensively in the field of Banking and Financial Services and this presentation will give a brief and fundamental highlight to the evergreen field.
MEDICI’s new ‘Open Banking’ report is a detailed analysis of the Open Banking landscape. Read about the evolution of Open Banking, the regulatory landscape, critical factors affecting the implementation of Open Banking, partnerships, market dynamics, and more!
This analysis provides an overview of the top trends in the retail banking sector driven by the competition, digital transformation, and innovation led by retail banks exploring novel ways to create and retain value in evolving landscape.
COVID-19 caught banks off guard and shook legacy mindsets to the core. With 20/20 (2020) hindsight, firms are more aware, digitally resilient, and financially stable as they head into 2022. The trials of the past 18 months forced firms to shore up existing business and consider new models and revenue streams.
Customer-centricity remains at the top of most FS agendas and is a 2022 focal point. Banks will focus on achieving operational excellence as diligently as delivering superior CX. In 2022 and beyond, it will be paramount for FIs to explore and invest in new technologies to remain relevant and resilient.
Banking 4.X will arrive in full force in 2022 with platform-supported firms monetizing diverse ecosystem capabilities and aggressively harvesting data to create experiential customer journeys through intelligent and personalized engagements. The new era will compel future-focused banks to finally abandon legacy infrastructure and collaborate with third-party specialists to solidify their best-fit, long-term roles. Increasingly, open platforms will make banks invisible as banking becomes embedded into customer lifestyles. At the same time, banks will shed asset-heavy models and shift to the cloud for greater agility, speed to market, and faster innovation. The shift will act as a precursor to adopting new technologies on the horizon – 5G and Decentralized Finance.
The recent past was filled will extraordinary lessons for financial institutions. Now is the time to act on those learnings and move forward profitably.
Open Banking - Opening the door to Digital Transformation WSO2
The open data era is just beginning in Australia. While the main purpose of the Consumer Data Right is to provide consumers, better control over their data, it is also paving the way for banks and financial institutions to step into newer business models through digital transformation. This talk will detail how banks stand to benefit from an open data ecosystem with a winning strategy and the right tools to achieve it. It will discuss the following topics:
The journey from Open APIs to Open Banking - An ecosystem for digital transformation
Building an open banking strategy for long term success
Realising the digital transformation opportunities of Open Banking
Loan Default Prediction with Machine LearningAlibaba Cloud
See webinar recording of this presentation at: https://resource.alibabacloud.com/webinar/detail.htm?webinarId=50
This webinar is designed to help users understand the end-to-end data science processes of using a propensity model on Alibaba Cloud’s Machine Learning Platform for AI; from defining the business problem, exploratory data analysis, data processing, model training to testing and deployment. You get an end-to-end case study (including a live demo) on how to use Alibaba Cloud products to predict the propensity of loan defaults.
Learn more about Machine Learning Platform for AI:
https://www.alibabacloud.com/product/machine-learning
Digital transformation of the banking industry Frank Schwab
From traditional to digital banking
Significantly changing basic conditions
New customer expectations and journeys
New digital products: crowd, P2P & crypto
New game changing technologies, processes and concepts: Cloud, API, blockchain, AI, platform, eco-systems, 100% STP
New types of leadership
This study notes will give you the complete knowledge about Centralized Online Real-Time Environment Banking System. From initially required knowledge to like how the bank works with the list of primary operation it also explains the detailed architecture of banking system with all relevant parameters. In addition, it also gives you the detail like audit procedure with relevant controls. Also gives you the required knowledge of IT Act and Cyber Frauds and more.
Aguai Solutions Perspective on New Age Digital Lending. Leverage the power of Digital Infrastructures to offer a Convenient of Lending to the right consumers through right Digital 30 degree of Credit Risk scores
In conjunction with Accenture, the Overseas Bankers Association of Australia (OBAA) Committee hosted a Thought Leadership Event in early August for OBAA members on the topic of Open Banking. Accenture has been spearheading research into the global adoption of Open Banking and the way in which it could revolutionise how banks generate value.
Find out more here: https://www.accenture.com/au-en/insight-open-banking-brave-new-world
Open Banking - The Digital Transformation Opportunity in Disguise WSO2
Seshika Fernando, head of financial solutions at WSO2, session at Bank Tech Asia - Colombo on “Open Banking: The Digital Transformation Opportunity in Disguise.” Seshika’s talk with cover the following:
A cross border transfer of experiences: What EU and UK banks have taught us
A 360 degree perspective of global open banking
How to break the barriers for a successful open banking strategy
Why open banking and digital transformation belong in the same sentence
Digital Banking - Industry Trends for Customer ServiceGianluca Ferranti
Consumers’ attitude and benefits of digital banking
Importance of real-time customer interaction in digital banking
Video Banking goes Prime Time
The opportunity for video-enabled interaction to transform retail banking
Revolut is transforming traditional banks! This slide show will help you understand what Revolut is. Differences with other banks. Its strengths and weaknesses.
To take advantage of Revolut's free card, don't forget to register on this dedicated link: https://www.revolut.com/referral/jrmy40htc
The demand for embedded finance is rising and Banking as a Service (BaaS) with APIs and strong risk and compliance capability is offering bundled service, generally white-labeled or cobranded services for non banks to serve their customers.
E Payment System Introduction Of Large Value Payment SystemHai Vu
- Basic concept of the Inter Bank Payment System.
- Explain on the basics of Real Time Settlement System.
- Payment system in Vietnam.
- Payment system in Nigeria
- Current trend of the Large Value Payment System using other settlement method.
The path to a Modern Data Architecture in Financial ServicesHortonworks
Delivering Data-Driven Applications at the Speed of Business: Global Banking AML use case.
Chief Data Officers in financial services have unique challenges: they need to establish an effective data ecosystem under strict governance and regulatory requirements. They need to build the data-driven applications that enable risk and compliance initiatives to run efficiently. In this webinar, we will discuss the case of a global banking leader and the anti-money laundering solution they built on the data lake. With a single platform to aggregate structured and unstructured information essential to determine and document AML case disposition, they reduced mean time for case resolution by 75%. They have a roadmap for building over 150 data-driven applications on the same search-based data discovery platform so they can mitigate risks and seize opportunities, at the speed of business.
This study notes will give you the complete knowledge about Centralized Online Real-Time Environment Banking System. From initially required knowledge to like how the bank works with the list of primary operation it also explains the detailed architecture of banking system with all relevant parameters. In addition, it also gives you the detail like audit procedure with relevant controls. Also gives you the required knowledge of IT Act and Cyber Frauds and more.
Aguai Solutions Perspective on New Age Digital Lending. Leverage the power of Digital Infrastructures to offer a Convenient of Lending to the right consumers through right Digital 30 degree of Credit Risk scores
In conjunction with Accenture, the Overseas Bankers Association of Australia (OBAA) Committee hosted a Thought Leadership Event in early August for OBAA members on the topic of Open Banking. Accenture has been spearheading research into the global adoption of Open Banking and the way in which it could revolutionise how banks generate value.
Find out more here: https://www.accenture.com/au-en/insight-open-banking-brave-new-world
Open Banking - The Digital Transformation Opportunity in Disguise WSO2
Seshika Fernando, head of financial solutions at WSO2, session at Bank Tech Asia - Colombo on “Open Banking: The Digital Transformation Opportunity in Disguise.” Seshika’s talk with cover the following:
A cross border transfer of experiences: What EU and UK banks have taught us
A 360 degree perspective of global open banking
How to break the barriers for a successful open banking strategy
Why open banking and digital transformation belong in the same sentence
Digital Banking - Industry Trends for Customer ServiceGianluca Ferranti
Consumers’ attitude and benefits of digital banking
Importance of real-time customer interaction in digital banking
Video Banking goes Prime Time
The opportunity for video-enabled interaction to transform retail banking
Revolut is transforming traditional banks! This slide show will help you understand what Revolut is. Differences with other banks. Its strengths and weaknesses.
To take advantage of Revolut's free card, don't forget to register on this dedicated link: https://www.revolut.com/referral/jrmy40htc
The demand for embedded finance is rising and Banking as a Service (BaaS) with APIs and strong risk and compliance capability is offering bundled service, generally white-labeled or cobranded services for non banks to serve their customers.
E Payment System Introduction Of Large Value Payment SystemHai Vu
- Basic concept of the Inter Bank Payment System.
- Explain on the basics of Real Time Settlement System.
- Payment system in Vietnam.
- Payment system in Nigeria
- Current trend of the Large Value Payment System using other settlement method.
The path to a Modern Data Architecture in Financial ServicesHortonworks
Delivering Data-Driven Applications at the Speed of Business: Global Banking AML use case.
Chief Data Officers in financial services have unique challenges: they need to establish an effective data ecosystem under strict governance and regulatory requirements. They need to build the data-driven applications that enable risk and compliance initiatives to run efficiently. In this webinar, we will discuss the case of a global banking leader and the anti-money laundering solution they built on the data lake. With a single platform to aggregate structured and unstructured information essential to determine and document AML case disposition, they reduced mean time for case resolution by 75%. They have a roadmap for building over 150 data-driven applications on the same search-based data discovery platform so they can mitigate risks and seize opportunities, at the speed of business.
Data Science Use Cases in The Banking and Finance SectorSofiaCarter4
Utilizing data science in the banking and financial industry is no longer merely a fad. Data science is having a significant impact on the banking and financial sectors. Let's take a quick look at this trend.
Data analytics is an essential area for the successful running of investment banking. Gain good knowledge of it to excel in the investment banking career
The banking industry is data-demanding with acknowledged ATM and credit processing data. As banks face increasing pressure to stay successful, understanding customer needs and preferences becomes a critical success factor. Along with Data mining and advanced analytics techniques, banks are furnished to manage market uncertainty, minimize fraud, and control exposure risk.
Driven by challenges on competition, rising customer expectation and shrinking
margins, banks have been using technology to reduce cost. Apart from competitive
environment, there has been deregulation as to rate of interest, technology intensive
delivery channel like Internet Banking, Tele Banking, Mobile banking and Automated
Teller Machines (ATMs) etc have created a multiple choice to user of the bank. The
banking business is becoming more and more complex with the changes emanating from
the liberalization and globalization. For a new bank, customer creation is important, but
an established bank it is the retention is much more efficient and cost effective
mechanism.
Embrace emerging trends, prioritize operational excellence, and choose the right partners like top credit card processors to empower your financial service offerings and thrive in the exciting world of credit cards. Visit us at: https://webpays.com/credit-card-processing.html
AI powered Decision Making in Banks - How Banks today are using Advanced analytics in credit Decisioning, enhancing customer life time value, lower operating costs and stronger customer acquisition
Supervised and unsupervised data mining approaches in loan default prediction IJECEIAES
Given the paramount importance of data mining in organizations and the possible contribution of a data-driven customer classification recommender systems for loan-extending financial institutions, the study applied supervised and supervised data mining approaches to derive the best classifier of loan default. A total of 900 instances with determined attributes and class labels were used for the training and cross-validation processes while prediction used 100 new instances without class labels. In the training phase, J48 with confidence factor of 50% attained the highest classification accuracy (76.85%), k-nearest neighbors (k-NN) 3 the highest (78.38%) in IBk variants, naïve Bayes has a classification accuracy of 76.65%, and logistic has 77.31% classification accuracy. k-NN 3 and logistic have the highest classification accuracy, F-measures, and kappa statistics. Implementation of these algorithms to the test set yielded 48 non-defaulters and 52 defaulters for k-NN 3 while 44 non-defaulters and 56 defaulters under logistic. Implications were discussed in the paper.
How Are Data Analytics Used In The Banking And Finance Industries.pdfMaveric Systems
amplify business success. Today, banks want more than incremental gains. They want datadriven revenue breakthroughs. Banks increasingly rely on data. It’s the future of communication
While traditional banks contend with inflexible legacy IT systems, the transformational ones deploy Agile methods to significantly reduce their time to value and make the organization more flexible as a whole.
Transformation is difficult and digital transformation is even harder.
With flickery markets, edgy economy, organizational change and the evolving regulatory landscape, the finance divisions are caught up in a fast increase in the amount of public support and changes. All this while, the need for cost cutting and delivering transparent reports stays stable. Rolta’s Financial Analytics solution CFO Impact helps you bring cost effective and sustainable transformations to financial processes and systems with the help of big data analytic technologies.
A potential objective of every financial organization is to retain existing customers and attain new
prospective customers for long-term. The economic behaviour of customer and the nature of the
organization are controlled by a prescribed form called Know Your Customer (KYC) in manual banking.
Depositor customers in some sectors (business of Jewellery/Gold, Arms, Money exchanger etc) are with
high risk; whereas in some sectors (Transport Operators, Auto-delear, religious) are with medium risk;
and in remaining sectors (Retail, Corporate, Service, Farmer etc) belongs to low risk. Presently, credit risk
for counterparty can be broadly categorized under quantitative and qualitative factors. Although there are
many existing systems on customer retention as well as customer attrition systems in bank, these rigorous
methods suffers clear and defined approach to disburse loan in business sector. In the paper, we have used
records of business customers of a retail commercial bank in the city including rural and urban area of
(Tangail city) Bangladesh to analyse the major transactional determinants of customers and predicting of a
model for prospective sectors in retail bank. To achieve this, data mining approach is adopted for
analysing the challenging issues, where pruned decision tree classification technique has been used to
develop the model and finally tested its performance with Weka result. Moreover, this paper attempts to
build up a model to predict prospective business sectors in retail banking.
GRC and Anti-Money Laundering Services.pdfbasilmph
Anti-money laundering services have been a part of compliance activities and processes in financial institutions for a long time. With the complexity and sophistication of the global financial system, anti-money laundering regulations are becoming more important.
This paper was presented at the Future of SMEs Banking Conference organised by Business a.m on 27th November, 2019 in Lagos. For SMEs to be able to play the role of engine of growth, Banks and other financial services provider need to be creative in managing funding and credit risks.
Similar to Applications of Data Science in Banking and Financial sector.pptx (20)
when will pi network coin be available on crypto exchange.DOT TECH
There is no set date for when Pi coins will enter the market.
However, the developers are working hard to get them released as soon as possible.
Once they are available, users will be able to exchange other cryptocurrencies for Pi coins on designated exchanges.
But for now the only way to sell your pi coins is through verified pi vendor.
Here is the telegram contact of my personal pi vendor
@Pi_vendor_247
The European Unemployment Puzzle: implications from population agingGRAPE
We study the link between the evolving age structure of the working population and unemployment. We build a large new Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices, and aggregate shocks. Once calibrated to the European economy, we quantify the extent to which demographic changes over the last three decades have contributed to the decline of the unemployment rate. Our findings yield important implications for the future evolution of unemployment given the anticipated further aging of the working population in Europe. We also quantify the implications for optimal monetary policy: lowering inflation volatility becomes less costly in terms of GDP and unemployment volatility, which hints that optimal monetary policy may be more hawkish in an aging society. Finally, our results also propose a partial reversal of the European-US unemployment puzzle due to the fact that the share of young workers is expected to remain robust in the US.
USDA Loans in California: A Comprehensive Overview.pptxmarketing367770
USDA Loans in California: A Comprehensive Overview
If you're dreaming of owning a home in California's rural or suburban areas, a USDA loan might be the perfect solution. The U.S. Department of Agriculture (USDA) offers these loans to help low-to-moderate-income individuals and families achieve homeownership.
Key Features of USDA Loans:
Zero Down Payment: USDA loans require no down payment, making homeownership more accessible.
Competitive Interest Rates: These loans often come with lower interest rates compared to conventional loans.
Flexible Credit Requirements: USDA loans have more lenient credit score requirements, helping those with less-than-perfect credit.
Guaranteed Loan Program: The USDA guarantees a portion of the loan, reducing risk for lenders and expanding borrowing options.
Eligibility Criteria:
Location: The property must be located in a USDA-designated rural or suburban area. Many areas in California qualify.
Income Limits: Applicants must meet income guidelines, which vary by region and household size.
Primary Residence: The home must be used as the borrower's primary residence.
Application Process:
Find a USDA-Approved Lender: Not all lenders offer USDA loans, so it's essential to choose one approved by the USDA.
Pre-Qualification: Determine your eligibility and the amount you can borrow.
Property Search: Look for properties in eligible rural or suburban areas.
Loan Application: Submit your application, including financial and personal information.
Processing and Approval: The lender and USDA will review your application. If approved, you can proceed to closing.
USDA loans are an excellent option for those looking to buy a home in California's rural and suburban areas. With no down payment and flexible requirements, these loans make homeownership more attainable for many families. Explore your eligibility today and take the first step toward owning your dream home.
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the contact information for my personal pi vendor.
Telegram: @Pi_vendor_247
Introduction to Indian Financial System ()Avanish Goel
The financial system of a country is an important tool for economic development of the country, as it helps in creation of wealth by linking savings with investments.
It facilitates the flow of funds form the households (savers) to business firms (investors) to aid in wealth creation and development of both the parties
If you are looking for a pi coin investor. Then look no further because I have the right one he is a pi vendor (he buy and resell to whales in China). I met him on a crypto conference and ever since I and my friends have sold more than 10k pi coins to him And he bought all and still want more. I will drop his telegram handle below just send him a message.
@Pi_vendor_247
what is the best method to sell pi coins in 2024DOT TECH
The best way to sell your pi coins safely is trading with an exchange..but since pi is not launched in any exchange, and second option is through a VERIFIED pi merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and pioneers and resell them to Investors looking forward to hold massive amounts before mainnet launch in 2026.
I will leave the telegram contact of my personal pi merchant to trade pi coins with.
@Pi_vendor_247
how to sell pi coins effectively (from 50 - 100k pi)DOT TECH
Anywhere in the world, including Africa, America, and Europe, you can sell Pi Network Coins online and receive cash through online payment options.
Pi has not yet been launched on any exchange because we are currently using the confined Mainnet. The planned launch date for Pi is June 28, 2026.
Reselling to investors who want to hold until the mainnet launch in 2026 is currently the sole way to sell.
Consequently, right now. All you need to do is select the right pi network provider.
Who is a pi merchant?
An individual who buys coins from miners on the pi network and resells them to investors hoping to hang onto them until the mainnet is launched is known as a pi merchant.
debuts.
I'll provide you the Telegram username
@Pi_vendor_247
where can I find a legit pi merchant onlineDOT TECH
Yes. This is very easy what you need is a recommendation from someone who has successfully traded pi coins before with a merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi network coins and resell them to Investors looking forward to hold thousands of pi coins before the open mainnet.
I will leave the telegram contact of my personal pi merchant to trade with
@Pi_vendor_247
Financial Assets: Debit vs Equity Securities.pptxWrito-Finance
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
how can I sell pi coins after successfully completing KYCDOT TECH
Pi coins is not launched yet in any exchange 💱 this means it's not swappable, the current pi displaying on coin market cap is the iou version of pi. And you can learn all about that on my previous post.
RIGHT NOW THE ONLY WAY you can sell pi coins is through verified pi merchants. A pi merchant is someone who buys pi coins and resell them to exchanges and crypto whales. Looking forward to hold massive quantities of pi coins before the mainnet launch.
This is because pi network is not doing any pre-sale or ico offerings, the only way to get my coins is from buying from miners. So a merchant facilitates the transactions between the miners and these exchanges holding pi.
I and my friends has sold more than 6000 pi coins successfully with this method. I will be happy to share the contact of my personal pi merchant. The one i trade with, if you have your own merchant you can trade with them. For those who are new.
Message: @Pi_vendor_247 on telegram.
I wouldn't advise you selling all percentage of the pi coins. Leave at least a before so its a win win during open mainnet. Have a nice day pioneers ♥️
#kyc #mainnet #picoins #pi #sellpi #piwallet
#pinetwork
BYD SWOT Analysis and In-Depth Insights 2024.pptxmikemetalprod
Indepth analysis of the BYD 2024
BYD (Build Your Dreams) is a Chinese automaker and battery manufacturer that has snowballed over the past two decades to become a significant player in electric vehicles and global clean energy technology.
This SWOT analysis examines BYD's strengths, weaknesses, opportunities, and threats as it competes in the fast-changing automotive and energy storage industries.
Founded in 1995 and headquartered in Shenzhen, BYD started as a battery company before expanding into automobiles in the early 2000s.
Initially manufacturing gasoline-powered vehicles, BYD focused on plug-in hybrid and fully electric vehicles, leveraging its expertise in battery technology.
Today, BYD is the world’s largest electric vehicle manufacturer, delivering over 1.2 million electric cars globally. The company also produces electric buses, trucks, forklifts, and rail transit.
On the energy side, BYD is a major supplier of rechargeable batteries for cell phones, laptops, electric vehicles, and energy storage systems.
2. Table of contents
• About the Domain
• Why the Banking domain is Essential in Finance
• Services Provided by the Banking Institutions
• Major Risks Faced by the Banking Institutions
• Applications of Data Science in Solving the Problems of the Domain
• Real Life Example: JP morgan's Use of Data Analytics
• Challenges and Use cases of the Banking Domain Testing
• Conclusion
3. The Domain - Banking and Finance
The financial sector is the segment of the economy comprising businesses
and institutions that offer financial services to both consumer and corporate
clients. An economy is in good shape if its financial sector is vital. The sector
comprises many sectors, including banking, investing, insurance, and real
estate organizations.
Some of the most well-known banking institutions in the world are among
the biggest firms in the financial industry, including the following:
• JPMorgan Chase (JPM)
• Wells Fargo (WFC)
• Bank of America (BAC)
• Citigroup (C)
4. Why the Banking domain is important in Finance
• The Banking domain comprises all the components that are required to run
financial service end to end. It encompasses the transaction and distribution
process; the manner in which consumers engage with the system, the goods,
and services the business delivers; and the technology involved.
• The area of technology and processes is the area of banking that is the widest.
The technology utilized to meet performance goals, the manner the
organization manages its personnel, their roles and duties, and the procedures
that clients must adhere to in order to complete a transaction are all included.
• Company managers can set the checkpoints required to enhance the
institution's performance by using a banking system as a framework. The
elements of the banking domain like the customers, the particular niche of
banking institutions, products and services, distribution and sales, and
technology, are widely relied upon by both developers and testers of financial
applications.
5. Services Provided by Banking Institutions
Banking institutions involve advancing investment banking
solutions for various businesses, organizations, and
governments, such as mergers and acquisitions, capital raising,
and risk management. They also offer insights into investment
banking and the larger fields of finance, economics, and
markets. Healthcare, technology, mergers and acquisitions
(M&A), shareholder involvement, and other industries are
covered.
A firm's interactions with the India-based subsidiaries, branches,
liaison offices, or project offices of its American clients are
managed by Commercial Bank International in India (CB-
India). As customers increase their presence in India, the
organizations part of this domain, offer them local knowledge,
consultative assistance, and complete banking solutions.
6. Major Risks Faced by the Banking Sector are:
Credit risk The most prominent risk facing banks is credit risk. When
counterparties or borrowers breach contractual duties, it happens. One
instance is when borrowers fail to make a loan payment for the principal
or interest. Mortgages, credit cards, and fixed-income assets are all
subject to default. Derivatives and offered guarantees are other instances
where obligations may not be met.
Operational risk is the possibility of suffering losses due to inaccuracies,
errors, or damage brought on by people, systems, or procedures.
Operational risk is lower for straightforward business activities like retail
banking and asset management and greater for activities like sales and
trading. Internal fraud and transaction errors are examples of losses
brought on by human error. On a larger scale, fraud can occur by
breaching a bank’s cybersecurity.
7. Market risk mainly results from a bank's capital market activity. Credit
spreads, interest rates, commodity prices, and equity markets are
unpredictable.
Liquidity risk The capacity of a bank to get money to satisfy financing
obligations are referred to as liquidity risk. If a bank delay giving some of
its clients cash for a day, other depositors can rush to withdraw their
money as they lose faith in the bank. Overreliance on short-term funding
sources, a balance sheet heavily weighted in illiquid assets, and a decline
in client confidence in the bank are some causes of banks' liquidity issues.
8. Banking analytics refers to the use of artificial intelligence and machine
learning to analyse customer data and make choices in the banking industry.
Using such analytics, data is examined, patterns are found, and forecasts are
created.
The banking domain needs to be aware of the must-have characteristics a
successful software tool has to offer like:
1. Secure user authentication mechanisms
2. Built-in management system
3. QR payment support
4. ATM locator
5. Real time processing and batch processing
9.
10. Risk Analysis
The banking sector places a strong focus on risk
modeling. It aids them in developing fresh methods of
performance evaluation. One of its most crucial
components is credit risk modeling. Banks can use
credit risk modeling to examine how loans will be
repaid. There is a possibility that the borrower won't be
able to pay back the loan in credit hazards.
Credit risk is complicated for banks to manage due to
its many variables. Banks can use risk modeling to
analyze the default rate and create plans to strengthen
their lending programs. Before authorizing a loan in a
high-risk situation, banking companies can analyze and
categorize defaulters with the use of big data and data
science.
Applications of Data Science in solving Problems in the
Banking & Financial Sector
11. Fraud Detection
Machine learning breakthroughs have made it simpler for businesses to identify
fraud and anomalies in transactional patterns. Monitoring and analyzing user
behavior for predictable or harmful patterns is part of fraud detection. Utilizing
data science, businesses may develop clustering tools that will aid in identifying
various trends and patterns in the ecosystem for fraud detection. These tools will
make use of machine learning and predictive analytics. Different methods, such
as K-means clustering and SVM, are useful in constructing the framework for
identifying odd activity and transaction patterns.
Customer Analytics
Banks can use predictive analytics to categorize potential clients and assign them
with high future value so that the firm can focus on them. While the
categorization algorithms assist the banks in attracting new clients, keeping them
is a difficult challenge. Various tools are employed in the preprocessing, cleaning,
and predicting data. They include Generalized Linear Models (GLM),
Classification and Regression Trees (CART), etc.
12. Customer Segmentation
Banks divide their customer base to serve client needs depending on their
behaviors and shared traits. In this case, segmenting clients based on similar
behaviors and identifying future customers rely heavily on machine learning
techniques like classification and clustering. K-means is a well-liked clustering
method that is frequently employed for grouping related data points. Customer
segmentation can be helpful to financial institutions in the ways of identifying
customers depending on how profitable they are, dividing clients into groups
according to how they use banking services, and strengthening their connections
with their clients.
Recommendation Engines
One of the key functions of a bank is to offer clients individualized experiences.
Offers and additional services are suggested using consumer transactions and
personal information data. After reviewing past transactions, banks also make an
educated guess as to what items the consumer could be interested in purchasing.
13. Predictive and real-time Analytics
The practise of utilising computer methods to forecast future occurrences is
known as predictive analytics. Predictive analytics' primary toolkit is machine
learning. The best instrument for enhancing the banks' analytical approach is
machine learning.
Data analysis is more important than ever because of the exponential growth of
data, which has led to a plethora of use cases.
14. JP MORGAN'S USE OF DATA ANALYTICS
JP Morgan uses Hadoop to analyze data and detect fraudulent activities, to add
value to the consumers. it uses predictive analysis to forecast its clients' effective
cash management practices.
JP Morgan's clients may get clear information using the "CreditMap" application.
Real-time analytics are offered to the clients via the Datawatch platform. the
organization uses big data analytics to use public information and help
policymakers prevent financial disasters.
JPMorgan explores, combines, and securely analyses various cyber datasets using
Sqrrl's big data analytics platform. JP Morgan uses big data to read the US
economy, for fraud detection, to get a clear perspective of credit market data, for
effective cash management, and to enrich customer experience.
15. Challenges and Use cases in Banking Domain Testing
Testing the applications in the banking domain is challenging. Assessing these
tools requires a high level of financial expertise and knowledge in data analysis.
Following are some challenges that are popularly faced by analysts while testing
applications in this domain:
• Implementing a strict security system
• Complex database
• Integrations with other tools
• Real-time data support
• Active device support
16. Conclusion
Testing banking domain applications is essential since it gives business owners
perspectives they might not have known before. It is preferable to take time
and identify every problem while the project is still in development rather than
correcting problems in a hurried atmosphere after the app has been released.
Banks must recognize the critical role of data science, incorporate it into their
decision-making process, and create strategies based on useful insights from
their client's data to acquire a competitive edge.
The approaches and tools provided by data science may increase the precision
of risk management, and the caliber of customer service, as well as automate
and speed up various business operations, so improving the organization's
overall efficiency. Adopting innovative techniques and algorithms for handling
timely information is crucial for remaining competitive and boosting
profitability.