Generative AI in Finance: Reality, Hype, and What's Next Anushree Shinde

Generative AI in Finance: Reality, Hype, and What's Next  Anushree Shinde

In 2023, ChatGPT, a conversational AI model created by OpenAI, ruled the tech conversation. Players in the payments, banking, and insurance industries are keen to grasp the potential of generative AI and put it to use.

Particularly in the payment, banking, and insurance sectors, generative AI has the potential to revolutionise financial services. The personalised marketing and experience, process automation, fraud detection, risk assessment, customer success, and product creation are the most promising use cases in the financial services industry. Even while generative AI has many useful applications, it is not yet appropriate for compliance, decision-making, or high-risk tasks like protecting payment systems or controlling trading.

We'll examine the realities of generative AI in banking in this debate, distinguish marketing hype from true development, and speculate on the potential of this technology.

Reality:

Fraud detection: Through pattern analysis, generative AI algorithms have the potential to spot potentially fraudulent behaviour in financial transactions, hence reducing risk.

Trading Techniques: Generative AI models examine historical market data to find patterns, trends, and correlations that could improve risk management and investing decisions.

Portfolio optimisation: Taking into account elements like past performance, risk appetite, and investment limits, generative AI helps to build diverse investment portfolios that strike a balance between risk and return.

Hype:

Data Overemphasis: The excitement around generative AI frequently obscures the difficulties in obtaining and cleaning huge datasets, potential biases, and data constraints that may affect model accuracy.

Challenges with Interpretability: In the financial sector, where accountability is vital, the employment of deep learning techniques in generative AI makes it difficult to understand and explain the reasoning behind model decisions.

Limited Adoption: Because of regulatory issues, a lack of confidence, and the requirement for extensive testing and validation, generative AI in finance has not yet been widely adopted in the real world.

What's Next:

Ethics: As generative AI develops, privacy protection, bias reduction, and adherence to legal and regulatory frameworks will become increasingly important to ensure responsible and ethical use.

Hybrid Approaches: Combining AI algorithms with human intuition and judgement, hybrid approaches combine generative AI models with human expertise to produce more reliable and comprehensible answers.

Improved Interpretability: In order to give clearer insights into the decision-making process of generative AI models, researchers are actively working on developing ways for greater interpretability, such as explainable AI methodologies.

Regulatory Frameworks: As regulatory organisations become more involved in regulating AI in finance, it is expected that they will set norms and rules to guarantee justice, accountability, and transparency in the application of generative AI.

In conclusion, it is critical to understand the constraints and practical potential of generative AI in finance. Addressing ethical issues, creating hybrid strategies, enhancing interpretability, and putting in place regulatory frameworks to enable responsible adoption are all necessary for the success of generative AI in the finance industry.

👍Anushree  Shinde[ MBA] 

Business Analyst

10BestInCity.com Venture

anushree@10bestincity.com

10bestincityanushree@gmail.com

www.10BestInCity.com 

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https://www.portrait-business-woman.com/2023/05/anushree-shinde.html

#GenerativeAI , #FinanceTechnology

#AIinFinance , #FraudDetection

#TradingStrategies , #PortfolioOptimization

#DataDrivenFinance , #EthicalAI

#Interpretability , #RealWorldAdoption

#HybridApproaches ,  #FutureOfFinance

#RegulatoryFrameworks , #InnovationInFinance

#ResponsibleAI

Empowering Senior Citizens through Social Media Marketing

Empowering Senior Citizens through Social Media Marketing

Job Description: 

Empowering Senior Citizens through Social Media Marketing

Position: Senior Citizen Social Media Marketing Specialist

Location: WORK FROM HOME

Job Summary: Are you a tech-savvy senior citizen yearning for a fulfilling opportunity to learn, explore, and make a positive impact? We are here to empower you with the skills and knowledge needed to become influential social media marketers. As a Senior Citizen Social Media Marketing Specialist, you will embark on an inspiring journey, where you'll not only discover the wonders of social media but also create a source of income while expressing your creativity and wisdom.

Responsibilities:

1. Embark on an exciting learning journey to master the art of social media marketing. We will provide comprehensive training, ensuring you are equipped with platform-specific best practices, content creation, community management, and influencer outreach.

2. Collaborate with our team of experts, who will guide you in developing innovative social media strategies customized for our company and clients' goals.

3. Unleash your creativity by creating and curating captivating content across various social media platforms like Facebook, Instagram, Twitter, LinkedIn, and YouTube.

4. Engage with followers, respond to comments and messages, and actively participate in online conversations, fostering a vibrant and supportive online community.

5. Immerse yourself in research to stay informed about industry trends, competitors' activities, and the preferences of our target audience, enabling you to optimize our social media strategies.

6. Collaborate with our talented design team to craft visually stunning and impactful social media graphics, videos, and other multimedia content.

7. Monitor and analyze social media performance metrics, identifying trends and proposing data-driven strategies to enhance engagement and reach.

8. Keep yourself up to date with the latest social media trends, platform updates, and emerging technologies, allowing you to continuously improve our online presence.

9. Participate in training sessions and workshops, where you'll witness your skills soar, as you gain expertise in social media marketing and influencer management.

10. Join our team meetings, where your valuable life experience and knowledge will make a significant impact. Share your ideas, contribute insights, and be part of a supportive and collaborative environment.

Qualifications:

1. Are you a senior citizen aged 55 or above, seeking an extraordinary opportunity to embrace your digital skills and unleash your creativity? We are eagerly waiting to empower you!

2. Possess a strong understanding of social media platforms such as Facebook, Instagram, Twitter, LinkedIn, and YouTube.

3. Your passion for technology and eagerness to learn will make you a tech-savvy individual who can quickly adapt to new tools and technologies.

4. Excellent written and verbal communication skills with an innate eye for detail will help you connect with our online community effectively.

5. Feel the enthusiasm for social media marketing, digital trends, and the power of online communities, as you embark on this inspiring journey.

6. Your creative mindset will enable you to generate engaging and visually appealing content that captures the hearts of our audience.

7. You are self-motivated, proactive, and possess the ability to work independently as well as collaboratively in a team environment.

8. Familiarity with basic graphic design tools like Canva or Adobe Photoshop is a plus, but not a requirement.

9. Previous experience in social media marketing or digital marketing is desirable but not necessary – your willingness to learn and adapt is what we value the most.

10. Embrace new technologies and industry changes with open arms, as we navigate this exciting path together.

Benefits:

• Experience comprehensive training in social media marketing and influencer management, equipping you with valuable skills and knowledge.

• Embrace the opportunity to build your personal brand and become a respected influencer in the online world.

• Enjoy a flexible work schedule that accommodates your preferences, providing a healthy

Unlimited Money Printing: A Recipe for Economic Disaster Aparna Thakur

 "Unlimited Money Printing: A Recipe for Economic Disaster "

Unlimited money printing, also known as hyperinflation, is a recipe for economic disaster. While it may seem tempting to simply print more money to address economic challenges, the consequences can be severe and far-reaching. This approach can lead to skyrocketing prices, devaluation of the currency, and a loss of confidence in the economy. To illustrate this point, we can examine the example of the United States during the COVID-19 pandemic.

The COVID-19 pandemic had a significant impact on the global economy, and the United States was no exception. In response to the economic downturn caused by the pandemic, the U.S. government implemented various stimulus measures to support businesses and individuals. These measures included massive fiscal spending, tax cuts, and an increase in government borrowing.

To finance these initiatives, the U.S. Federal Reserve, the country's central bank, resorted to quantitative easing, which essentially involves printing money. The Federal Reserve purchased large amounts of government bonds and other assets, effectively injecting trillions of dollars into the economy. While this was done with the intention of stimulating economic growth, it carried significant risks.

As the supply of money in circulation increased rapidly, the value of each dollar began to erode. When more money chases the same amount of goods and services, prices tend to rise. This phenomenon, known as inflation, started to occur in the United States. While moderate inflation is generally considered healthy for an economy, excessive inflation can have devastating consequences.

In the case of the United States during the COVID-19 pandemic, inflationary pressures began to mount. Prices of essential goods such as food, housing, and healthcare started to rise at an alarming rate. This placed a burden on consumers, especially those with fixed incomes, as their purchasing power diminished. Furthermore, businesses faced difficulties in planning and pricing their products due to the uncertainty caused by rapid price increases.

The devaluation of the currency also had implications beyond domestic markets. As the U.S. dollar lost value, its status as a global reserve currency was threatened. Foreign investors and central banks, who hold substantial amounts of U.S. dollars as reserves, could lose confidence in the currency, leading to a sell-off and further devaluation. This could have significant consequences for international trade, investment, and the overall stability of the global financial system.

Unlimited money printing, as exemplified by the United States during the COVID-19 pandemic, can be a recipe for economic disaster. While the intention may be to provide short-term relief and stimulate growth, the long-term consequences can be severe. Hyperinflation, rising prices, and the devaluation of the currency can erode the purchasing power of individuals, disrupt business operations, and undermine confidence in the economy.

It is crucial for governments and central banks to strike a balance between providing necessary economic support and maintaining the stability of the currency. Sustainable fiscal and monetary policies, coupled with prudent financial management, are key to avoiding the pitfalls of unlimited money printing. By carefully considering the potential risks and implementing measures to mitigate them, economies can navigate challenging times while preserving the long-term health and stability of their financial systems.

Aparna Thakur

(Fin-Tech manager)

10bestincity@gmail.com

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#economy, #business, #finance, #economics, #covid, #money, #india, #news, #stockmarket ,#investing, #investment, #politics, #coronavirus, #indianeconomy, #stocks, #entrepreneur, #bitcoin, #economia, #trading, #usa, #trade, #education,  #invest, #gdp, #forex, #market, #motivation,  #wealth

@Reserve Bank of India @Federal Reserve system @ State Bank of India @Union Bank of India @Bank of Baroda @ UCO Bank @ Central Bank of India @Punjab National Bank

@Shekhar Gupta @10 Bestincity @Aparna Thakur

India's Digital Personal Data Protection Bill: Nearing Implementation in 2023

India's Digital Personal Data Protection Bill: Nearing Implementation in 2023

A lot is riding on the Digital India Bill as India looks to achieve its digital economy target of $1 trillion, or 20 per cent of the GDP, over time.

India's Digital Personal Data Protection Bill 2022 (DPDPB), which is aimed at safeguarding personal data, is getting closer to becoming a law. While it has been approved by the Parliamentary Standing Committee, it still needs to be officially enacted. However, organizations should get ready for its potential effects. Once it becomes a law, the DPDPB is anticipated to be strictly enforced, overseen by an active regulator. Therefore, organizations need to take proactive steps to ensure they comply with the new legislation.

The Bill will force every institution collecting, storing, transmitting, or processing data to apply the measures and protections set by the law, which is expected to have a significant impact on enterprises operating in India, particularly those that handle sensitive personal information.

The proposed Bill emphasises the user's consent, which is "deemed" and may be revoked at any time. Additionally, it calls for data fiduciaries—those who decide how and why to treat personal information—to keep data accurate and secure and to remove it once that goal has been achieved. According to Shreya Suri, Partner at IndusLaw, "With the introduction of consent of users, the Bill empowers them to have a say about how his or her data is being used," adding that the law will hold all stakeholders accountable for their data practises.

भारत का डिजिटल व्यक्तिगत डेटा संरक्षण विधेयक: 2023 में कार्यान्वयन के करीब

डिजिटल इंडिया विधेयक पर बहुत कुछ टिका हुआ है, क्योंकि भारत वित्तीय वर्ष 2023 तक अपने $1 ट्रिलियन के डिजिटल अर्थव्यवस्था लक्ष्य को प्राप्त करने की उम्मीद रख रहा है, जो GDP का 20 प्रतिशत होना है।

भारत का डिजिटल व्यक्तिगत डेटा संरक्षण विधेयक 2022 (DPDPB), जो व्यक्तिगत डेटा की सुरक्षा को ध्यान में रखने का उद्देश्य रखता है, कानून बनने के करीब पहुंच रहा है। हालांकि, यह संसदीय स्थायी समिति द्वारा मंजूरी प्राप्त कर लिया गया है, लेकिन इसे आधिकारिक रूप से प्रचलित किया जाना चाहिए। हालांकि, संगठनों को इसके संभावित प्रभावों के लिए तैयार होना चाहिए। जब यह कानून बन जाएगा, तो DPDPB का सख्ती से पालन किया जाने की उम्मीद है, जिसे सक्रिय नियामक द्वारा पर्यवेक्षित किया जाएगा। इसलिए, संगठनों को नए कानून का पालन करने के लिए सक्रिय कदम उठाने की आवश्यकता होती है।

यह विधेयक हर संस्थान को जो डेटा एकत्र, संग्रह, संक्रमण या प्रसंस्करण कर रहा है, को क़ानून द्वारा निर्धारित उपाय और संरक्षण को लागू करने के लिए मजबूर करेगा, जो कि भारत में संचालित उद्यमों पर एक महत्वपूर्ण प्रभाव होने की उम्मीद है, विशेष रूप से वे उद्यम जो संवेदनशील व्यक्तिगत जानकारी का संचालन करते हैं।

प्रस्तावित विधेयक उपयोगकर्ता की सहमति पर जोर देता है, जो "मान्य" माना जाता है और किसी भी समय रद्द किया जा सकता है। इसके अलावा, यह डेटा फिडुशिएरीज के लिए अपील करता है - वे हैं जो निर्धारित करते हैं कि व्यक्तिगत जानकारी का व्यवहार कैसे और क्यों करना है - डेटा को सटीक और सुरक्षित रखने के लिए और जब उस लक्ष्य को प्राप्त कर लिया जाए, तो इसे हटा देने के लिए कहता है। इंडसलॉ के साथी श्रेया सुरी के अनुसार, "उपयोगकर्ताओं की सहमति के प्रस्तावना के साथ, यह विधेयक उन्हें यह कहने की सामर्थ्य प्रदान करता है कि उनका डेटा कैसे और कहाँ उपयोग हो रहा है," और इसके अलावा यह उसकी जोगिता में सभी हितधारकों को जवाबदेहीपूर्ण बनाने का आदेश देता है।

👍Anushree Shinde

Anushree  Shinde[ MBA] 

Business Analyst

10BestInCity.com Venture

+91 9011586711

anushree@10bestincity.com

10bestincityanushree@gmail.com

www.10BestInCity.com 

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#IndiaDataProtection , #DigitalPrivacy

#PrivacyRights , #DataSovereignty

#PersonalDataProtection , #PrivacyLegislation

#DataPrivacyLaw , #DataProtectionIndia

#DataPrivacyRights , #DataPrivacyBill

#IndiaDataSecurity , #DigitalRights

#PrivacyLaws , #DataGovernance

#DataPrivacyIndia , #DataProtectionFramework

#DataOwnership , #PrivacyAwareness

Mastering LinkedIn Analytics: A Comprehensive Guide for Marketers

Mastering LinkedIn Analytics: A Comprehensive Guide for Marketers

For marketers trying to engage with professionals, increase brand awareness, and create leads, LinkedIn has emerged as a critical platform. Understanding and utilising the potential of LinkedIn Analytics is crucial for getting the most out of your LinkedIn marketing efforts. In this thorough guide, we'll go through LinkedIn Analytics' main features and offer helpful advice so you can understand this useful tool.

1. Getting Started with LinkedIn Analytics

You need to have a Company Page or a personal profile with enough followers in order to access LinkedIn Analytics. Once you have the required access credentials, go to the Analytics section to start perusing the vast amount of data that is there.

2. Understanding LinkedIn Metrics

A variety of metrics are available on LinkedIn to assist you assess the effectiveness of your content and marketing efforts:

a. Impressions: This statistic shows how frequently LinkedIn users see your content. You can determine the audience reach of your posts and find popular material by keeping an eye on impressions.

b. Engagement: Metrics for engagement track the likes, comments, and shares that your content receives. It offers perceptions into how effectively your content connects with your audience and can direct the design of future material.

c. Click -Through Rates: indicates the proportion of visitors who clicked on a link or call-to-action within your content. It is an essential indicator for determining how well your LinkedIn campaigns are working and for increasing website traffic.

d. Follower Demographics: LinkedIn Analytics provides insightful data on your followers' geography, industry, jobs held, and other details. You may modify your material to better engage your audience by understanding their demographics.

3. Analyzing Content Performance

Your go-to area in LinkedIn Analytics to assess the performance of your posts, articles, and videos is the Content Performance tab. Here are some good ways to use it:

a.Determine the types of content that are most popular with your audience by analysing measures like engagement, impressions, and Click -Through Rates. To repeat success, look for trends and themes.

b. Track Trends: Analyse performance indicators over a range of time frames to spot patterns. You can use this study to make data-driven decisions and analyse the effects of your content strategy.

c. Experiment with Different forms: There are many different content forms that LinkedIn offers, including text posts, articles, photos, videos, and documents. Test various forms to determine which ones result in the most interaction, then modify your plan as necessary.

4. Tracking Follower Growth

You may track progress and gain a better understanding of your audience by using the Follower Analytics page, which offers insights regarding your follower base:

a. Follower Growth Over Time: Pay attention to the increase in followers your personal or company page experiences over time. This indicator enables you to evaluate the success of your follower acquisition and retention strategies.

b. Demographic Insights: LinkedIn Analytics provides useful information on the characteristics of followers, including location, industry, job titles, and more. Make better use of this data to target your audience and give more pertinent content.

c. Audience segmentation: To customise your content and messaging, divide your audience into groups based on their demographics or level of involvement. Content that is tailored to the reader is more likely to strike a chord and increase engagement.

5. Measuring Page Performance

The Page Analytics tab on the LinkedIn Company Page you manage offers information on how well it is performing:

a. Page Views and Unique Visitors a. Track the number of page views and unique visitors to your company page to determine its visibility and reach. Determine trends, then modify your content approach as necessary.

b. visitors Sources: LinkedIn Analytics reveals the sources of visitors to your page, including external links, LinkedIn search, and search engines. Identifying effective promotion channels requires an understanding of traffic sources.

c. Benchmarking Performance: Examine competitors' websites' performance to learn about their tactics. 

Marketing professionals looking to increase the effectiveness of their LinkedIn efforts must learn how to use LinkedIn Analytics. LinkedIn Analytics gives you access to a plethora of information and practical insights that you can use to track audience interaction, evaluate the success of your content, and better understand your followers. You can improve your content strategy, see patterns, and make data-driven decisions by continuously tracking and examining important metrics like impressions, engagement, click-through rates, and follower demographics.

👍Anushree  Shinde[ MBA] 

Business Analyst

10BestInCity.com Venture

anushree@10bestincity.com

10bestincityanushree@gmail.com

www.10BestInCity.com 

Linktree:https://linktr.ee/anushreeas?utm_source=linktree_profile_share

LinkedIn: https://www.linkedin.com/in/anushree-shinde20

Facebook: https://shorturl.at/hsx29

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Youtube: https://www.youtube.com/@10BestInCity

Email: info@10bestincity

https://www.portrait-business-woman.com/2023/05/anushree-shinde

https://www.bestinternationaleducation.com/2023/06/mastering-linkedin-analytics.html

#LinkedInAnalyticsMastery  , #LinkedInMarketingGuide 

#DataDrivenMarketing , LinkedInInsights 

#SocialMediaAnalytics  , #DigitalMarketingStrategy 

#LinkedInMetrics  , #OptimizeLinkedInMarketing 

#AudienceEngagement  , #LinkedInSuccessTips

10 Best Highest Paying Data Science Jobs in India, 2023 Anushree Shinde MBA

10 Best Highest Paying Data Science Jobs in India, 2023  Anushree  Shinde MBA

If you're interested in pursuing a career in data science, it is recommended to consider enrolling in an applied business analytics course. Such courses provide valuable knowledge on acquiring business data, preparing it for analysis, and utilizing analytics tools to analyze the data. This course specifically focuses on applying machine learning techniques to solve business problems in a professional setting.

Here are a few of the top-paying data science positions in India for the year 2023:

1. Database Manager: Responsible for maintaining an organization's databases, streamlining information and reporting, and identifying and resolving issues. National average salary: $65,558 per year.

2. Data Analyst: Analyzes and combines large volumes of data to provide solutions for businesses. They work across various industries such as healthcare, IT, professional sports, and finance, aiming to enhance efficiency, improve processes, and reduce costs. National average salary: $75,225 per year.

3. Data Warehouse Manager: Oversees the maintenance of data storage facilities, handles reporting errors, ensures data security, and maintains operational efficiency. National average salary: $84,221 per year.

4. Database Developer: Manages and updates databases, hardware, and software to optimize their performance. National average salary: $89,250 per year.

5. usiness Intelligence Analyst: Evaluates corporate data to identify patterns and anomalies, presenting findings to business executives and stakeholders to aid in decision-making. National average salary: $96,737 per year.

6. Database Administrator: Handles the day-to-day management of databases and ensures data security and accessibility for authorized personnel. National average salary: $97,730 per year.

7. Statistician: Collects and interprets large volumes of data to provide reports on trends, errors, discrepancies, and recommendations for company executives. National average salary: $99,055 per year.

8. Business Intelligence Developer: Constructs systems and programs that allow users to access and interact with required information within an organization. National average salary: $100,494 per year.

9. Infrastructure Engineer: Installs, operates, and troubleshoots servers, networks, and cloud computing systems for businesses. Also known as cloud engineers. National average salary: $113,546 per year.

10. Data Scientist: Assists businesses in identifying problems and develops new management frameworks and algorithms to address them. National average salary: $122,519 per year.

11. Data Modeler: Analyzes company trends and predicts future behavior using analytical and statistical knowledge. National average salary: $93,476 per year.

12. Big Data Engineer: Processes large amounts of data to transform it into usable information. National average salary: $132,571 per year.

13. Data Architect: Evaluates an organization's data sources and designs the systems and structures required for managing, collecting, analyzing, and safeguarding the data. National average salary: $133,840 per year.

In conclusion, the field of data science is causing significant changes in various industries. This article has provided an overview of the responsibilities and qualifications for some of the highest-paying data science positions

👍Anushree Shinde

Business Analyst

10BestInCity.com Venture

anushree@10bestincity.com

10bestincityanushree@gmail.com

www.10BestInCity.com 

Linktree:https://linktr.ee/anushreeas?utm_source=linktree_profile_share

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#DataScienceJobs,

#DataAnalyticsCareer,

#TopPayingJobs,

#IndiaDataScience,

#HighSalaryJobs,

#TechIndustry,

#CareerOpportunities,

#DataScientistsIndia,

#DataJobs2023,

#BusinessIntelligenceRoles,

TCS Drives AI Innovation Harnessing Vast Data Troves to Build ChatGPT-like Generative AI Technology Anushree Shinde MBA

 TCS Drives AI Innovation: Harnessing Vast Data Troves to Build ChatGPT-like Generative AI Technology  Anushree  Shinde[ MBA] 

Tata Consultancy Services (TCS) is embracing the rise of generative AI and GPT-like technologies by developing AI tools that can code complete solutions for enterprises. TCS COO N Ganapathy Subramaniam revealed in an interview that the company has been working on automation solutions for over two decades. Their cognitive automation product, MasterCraft, is capable of reusing and restructuring existing code to build solutions. While MasterCraft has been successful in generating code for specific applications, TCS's next goal is to integrate generative AI into the product to create software that can construct entire enterprise-level solutions. Subramaniam estimated that building these tools could take anywhere from six months to two years.

The introduction of generative AI features into TCS's solutions may have implications for coders at the company. TCS is renowned for hiring a large number of freshers each year who possess the technical knowledge to write scalable coding solutions. However, with the advancement of generative AI, these positions could become obsolete as the technology has the potential to generate complex algorithms capable of solving enterprise-level problems.

Experts believe that generative AI technologies hold great promise in creating intricate algorithms that can address complex enterprise-level challenges. Consequently, the development of tools with generative capabilities could present challenges for many coders at TCS. The company's large-scale hiring of freshers, who excel at writing basic code solutions, may be impacted by the advent of generative AI.

In order to leverage the full potential of AI, TCS anticipates the emergence of new roles like "prompt engineers." These individuals would work alongside AI systems to optimize their performance and ensure the best outcomes. This signifies a shift in TCS's approach as they strive to maximize the benefits offered by generative AI technologies while also adapting their workforce to accommodate these advancements.

टीसीएस एआई इनोवेशन चलाता है: चैटजीपीटी जैसी जनरेटिव एआई टेक्नोलॉजी बनाने के लिए विशाल डेटा ट्रोव्स का उपयोग करना

टाटा कंसल्टेंसी सर्विसेज (टीसीएस) एआई उपकरण विकसित करके जेनरेटिव एआई और जीपीटी जैसी प्रौद्योगिकियों के उदय को गले लगा रही है जो उद्यमों के लिए पूर्ण समाधान को कोड कर सकते हैं। टीसीएस के सीओओ एन गणपति सुब्रमण्यम ने एक साक्षात्कार में खुलासा किया कि कंपनी दो दशकों से अधिक समय से स्वचालन समाधान पर काम कर रही है। उनका संज्ञानात्मक स्वचालन उत्पाद, मास्टरक्राफ्ट, समाधान बनाने के लिए मौजूदा कोड का पुन: उपयोग और पुनर्गठन करने में सक्षम है। जबकि मास्टरक्राफ्ट विशिष्ट अनुप्रयोगों के लिए कोड उत्पन्न करने में सफल रहा है, टीसीएस का अगला लक्ष्य सॉफ्टवेयर बनाने के लिए उत्पाद में जेनरेटिव एआई को एकीकृत करना है जो पूरे उद्यम-स्तरीय समाधान का निर्माण कर सकता है। सुब्रमण्यम ने अनुमान लगाया कि इन उपकरणों के निर्माण में छह महीने से दो साल तक का समय लग सकता है।

टीसीएस के समाधानों में जेनरेटिव एआई सुविधाओं की शुरुआत से कंपनी में कोडर के लिए निहितार्थ हो सकता है। टीसीएस प्रत्येक वर्ष बड़ी संख्या में फ्रेशर्स को काम पर रखने के लिए प्रसिद्ध है, जिनके पास स्केलेबल कोडिंग समाधान लिखने के लिए तकनीकी ज्ञान है। हालांकि, जेनरेटिव एआई की प्रगति के साथ, ये पद अप्रचलित हो सकते हैं क्योंकि प्रौद्योगिकी में उद्यम-स्तरीय समस्याओं को हल करने में सक्षम जटिल एल्गोरिदम उत्पन्न करने की क्षमता है।

विशेषज्ञों का मानना है कि जेनरेटिव एआई प्रौद्योगिकियां जटिल एल्गोरिदम बनाने में बहुत वादा करती हैं जो जटिल उद्यम-स्तरीय चुनौतियों का समाधान कर सकती हैं। नतीजतन, उत्पादक क्षमताओं वाले उपकरणों का विकास टीसीएस में कई कोडर के लिए चुनौतियां पेश कर सकता है। कंपनी के बड़े पैमाने पर फ्रेशर्स की भर्ती, जो बुनियादी कोड समाधान लिखने में उत्कृष्टता प्राप्त करते हैं, जनरेटिव एआई के आगमन से प्रभावित हो सकते हैं।

एआई की पूरी क्षमता का लाभ उठाने के लिए, टीसीएस "शीघ्र इंजीनियरों" जैसी नई भूमिकाओं के उद्भव की उम्मीद करता है। ये व्यक्ति अपने प्रदर्शन को अनुकूलित करने और सर्वोत्तम परिणाम सुनिश्चित करने के लिए एआई सिस्टम के साथ काम करेंगे। यह टीसीएस के दृष्टिकोण में बदलाव को दर्शाता है क्योंकि वे इन प्रगति को समायोजित करने के लिए अपने कार्यबल को अनुकूलित करते हुए उत्पादक एआई प्रौद्योगिकियों द्वारा पेश किए गए लाभों को अधिकतम करने का प्रयास करते हैं।

👍Anushree Shinde

Business Analyst

10BestInCity.com Venture

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#TCSAIInnovation , #DataDrivenInnovation , #GenerativeAI ,#ChatGPTTechnology , #HarnessingData , #AIAdvancements ,#TCSInnovation , #DataTroves , #AIRevolution ,#InnovativeTechnology

Reverse EMI for MBA Professionals Aparna Thakur

Reverse EMI for MBA Professionals Aparna Thakur

In today's competitive job market, pursuing higher education has become a necessity for professionals looking to advance their careers. Among the various postgraduate degrees, the Master of Business Administration (MBA) stands out as a popular choice for many working professionals. However, the cost of pursuing an MBA can be a significant barrier for individuals who are already employed and have financial commitments. To address this challenge, the concept of reverse EMI (Equated Monthly Installment) has emerged as a viable option for MBA professionals. Reverse EMI allows individuals to pay for their education after completing their MBA program, making it an attractive alternative for those who cannot afford the upfront costs. This essay will explore the concept of reverse EMI for MBA professionals, its benefits and drawbacks, and its potential impact on the educational landscape.

What is Reverse EMI?

Reverse EMI is a financial arrangement that enables individuals to finance their education expenses after completing their studies. Unlike traditional EMI systems, where individuals pay installments during the course of their education, reverse EMI allows students to delay their payments until they have completed their program and started working. This approach helps ease the financial burden on students, especially working professionals who may have existing financial commitments. Under a reverse EMI scheme, individuals can pursue an MBA program without worrying about immediate repayment, allowing them to focus on their studies and career growth.

 Benefits of Reverse EMI for MBA Professionals

Reverse EMI offers several benefits to MBA professionals seeking to further their education. First and foremost, it provides them with the opportunity to gain valuable skills and knowledge through an MBA program without the need for immediate financial resources. This accessibility can be a game-changer for individuals who have long aspired to pursue an MBA but lacked the means to do so. Reverse EMI also enables professionals to continue earning a regular income while studying, as they do not have to quit their jobs to finance their education. This financial stability can alleviate the stress associated with managing both work and study commitments simultaneously.

 Drawbacks and Challenges of Reverse EMI

While reverse EMI offers significant advantages, it is not without its challenges. One of the key drawbacks is the potential increase in the overall cost of education. As individuals defer their payments, they may be subject to interest charges or additional fees, which can inflate the total amount they need to repay. Additionally, reverse EMI schemes may have eligibility criteria or limited availability, restricting access for certain individuals. Another challenge is the psychological burden of debt that individuals may face after completing their MBA. The pressure to secure a well-paying job to repay the loan can create additional stress and may impact career choices.

 Impact on the Educational Landscape

The introduction of reverse EMI for MBA professionals has the potential to reshape the educational landscape in several ways. Firstly, it can lead to increased enrollment in MBA programs, particularly among individuals who were previously unable to afford them. This can contribute to a more diverse and inclusive classroom environment, bringing in professionals from various backgrounds and industries. Additionally, the availability of reverse EMI may encourage more working professionals to pursue lifelong learning and continuous education. It can foster a culture of upskilling and professional development, enhancing the overall quality of the workforce.

 Reverse EMI for MBA professionals has emerged as a viable financial solution to address the cost barriers associated with pursuing an MBA degree. It allows individuals to postpone their education expenses until after completing their program, making it an attractive option for working professionals. While reverse EMI offers benefits such as accessibility and the ability to continue earning an income, it also has drawbacks like potential increased costs and the psychological burden of debt. However, overall, reverse EMI has the potential to make MBA education more accessible and contribute to the growth and development of professionals in today's competitive job market. As more educational institutions and financial institutions adopt this model, it is crucial to strike a balance between affordability and ensuring the long-term financial well-being of MBA professionals.

 

Aparna Thakur

(Fin-Tech manager)

10bestincity@gmail.com

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#reverse,#emi,#education,#mba,#installment,#saving,#investment,#fixedincome,#interset,#electronic,#monthly,#emidesign

@State Bank of India @Union Bank of India @Punjab National Bank @Canara Bank @Bank of Baroda @Yes Bank @Bandhan Bank @HDFC Bank @ICICI Bank @Axis Bank 

@Shekhar Gupta @10 Bestincity @Aparna Thakur

Less or Small Work is Crime in Corporate World

 Less or Small Work is Crime in Corporate World 

In the corporate world, the expectation is generally that employees will contribute to the organization's goals and work diligently to achieve them. While it may not be accurate to categorize less or small work as a "crime," per se, it is typically not viewed positively within a corporate environment.

In a competitive business landscape, companies strive for efficiency, productivity, and profitability. They rely on their employees to contribute their skills and efforts towards these objectives. When an employee consistently performs below expectations or fails to fulfill their responsibilities adequately, it can have detrimental effects on the company's overall performance.

While it is important to acknowledge that not all employees are equally capable or have the same workload, consistently producing less or smaller work compared to peers can lead to concerns about an individual's commitment, motivation, or competence. It can raise questions about their suitability for their current role or the organization as a whole.

In some cases, consistently underperforming or engaging in minimal effort can be considered a form of misconduct or negligence. It may violate employment contracts, ethical guidelines, or company policies. Employers have the right to expect a certain level of performance and productivity from their employees, and failure to meet these expectations can result in consequences such as warnings, reprimands, performance improvement plans, or even termination.

However, it is essential to consider individual circumstances and factors that may influence an employee's work output. Factors such as workload distribution, resource availability, personal circumstances, or health issues can all impact an employee's ability to perform at their best. It is important for employers to provide support, guidance, and opportunities for improvement before taking disciplinary actions.

Ultimately, while less or small work may not be classified as a crime, it is generally not conducive to a successful career in the corporate world. Employees are expected to demonstrate dedication, professionalism, and a commitment to achieving organizational goals. Meeting or exceeding performance expectations is important for personal growth, career advancement, and maintaining a positive reputation within the company.

Corporate Crime

What is corporate crime? Corporate crime, sometimes called white-collar crime, is crime committed by individuals on behalf of a business. The corporate crime definition indicates clearly that corporate crimes are committed by individuals to benefit the companies that employ them, although the individuals involved may themselves profit from such acts.

Criminologists have proposed several theories of corporate crime. The first formal explanation for corporate crime was put forward by criminologist Edwin Sutherland in 1939. His Theory of Differential Association suggests that criminal behavior occurs when individuals associate with others who view criminal acts in a positive light. Sutherland also claims that criminal behavior occurs only if individuals are in an environment where most associates do not view criminal behavior in a negative way.

Some criminologists have criticized the Theory of Differential Association, noting that it fails to consider other causes such as depression and drug abuse. Sutherland's theory also fails to account for differences among individuals.

Corporate Crime Examples

Corporate crime examples are plentiful and take many forms. Violation of environmental laws, bribery, false claims, and corporate fraud are a few examples of corporate crime; types of white-collar crimes are limited, of course, only by the criminal's imagination.

Individuals who violate environmental laws designed to protect the environment typically do so to increase corporate profits. Although the company may benefit from failing to abide by environmental regulations, in this type of case the individuals who instigate the crime likely expect to benefit indirectly as well through corporate bonuses, increased salary, or higher stock prices. An example of this type of crime can be found in a case involving Royal Caribbean Cruises Ltd. In 1999, the company was ordered to pay a record-breaking 18 million dollar fine for routinely dumping waste oil, a widespread practice that occurred throughout the company's fleet. Representatives of the company also lied to the Coast Guard regarding oil disposal by submitting falsified oil logs.

Bribery occurs when an individual offers something of value to another person to influence the recipient's actions. In 2014, Rite Aid Corporation was ordered to pay a 2.99 million dollar fine for offering gift cards to Medicare and Medicaid patients to influence them to transfer their prescriptions to Rite Aid pharmacies.

In the 1980s, Beech Nut settled a case that showed the company had made false claims when it claimed its apple juice was 100% fruit juice. In fact, the product was shown to consist primarily of sugar water with only a small amount of apple juice.

Corporate fraud involves the intentional misstatement of facts about the company's financial situation or its corporate actions. This kind of misrepresentation is intended to mislead the public or increase company profits. In the largest case of corporate fraud at the time, WorldCom, a Mississippi-based telecommunications company, was found to have overstated its earnings to drive up stock prices. These actions led to the company's bankruptcy and a 25-year prison sentence for WorldCom's CEO.

Corporations and criminality

Most criminologists divide white-collar crime into two major types: corporate crime and occupational crime (crime committed during the course of a legitimate occupation, for one’s own benefit). Most corporate criminals do not view their activities as criminal, since their violations are usually part of their occupational environment. Corporate offenders remain committed to conventional society and do not identify with criminality. Their inappropriate behavior is often informally approved by occupational or corporate subcultures.

Despite Sutherland’s pioneering study, little attention was focused on the white-collar variety until the first large-scale, comprehensive investigation of corporate crime, by American criminologists Marshall Clinard and Peter Yeager, titled Illegal Corporate Behavior, 1975–1976 (1979). The study involved a systematic investigation of administrative, civil, and criminal actions either filed or completed by 25 federal agencies against 477 of the largest wholesale, retail, and service organizations in the United States. Many of the same patterns that had been discovered by Sutherland some three decades earlier were found to persist. About 60 percent of the large corporations had at least one legal action initiated against them, while the most deviant firms—8 percent of the corporations—committed the majority of offenses (52 percent of all offenses). The oil, pharmaceutical, and automobile industries were responsible for almost half of all violations. The leniency with which corporate violators were treated persisted.

The challenge of combating corporate crime

While corporations may complain about the burden of federal bureaucracies and their enforcement of regulations, guilty companies generally have more expertise, staff, and time to devote to their defense than the government has for prosecution. Regulatory agencies have been criticized as being ineffective in enforcing laws against powerful corporations. Often the penalties for law violation are too small to act as deterrents. Offenders are seldom convicted and rarely get jail time. Many are permitted to plead nolo contendere (no contest) to charges, which enables them to escape the stigma of being labeled “guilty” or “criminal.” The appointed directors of agencies are often drawn from the very corporations to be regulated; these same companies may then hire retiring agency employees. In addition, the amount of money governments assign to corporate crime generally is much smaller than that allocated for street crime.

Sneha Jaiswal

MBA 

Founder of MBA Saree Wali

www.mbasareewali.com

Data Manipulation Techniques: From Raw Data to Insights

Data Manipulation Techniques: From Raw Data to Insights

Techniques for manipulating data are essential for turning raw data into insightful information. Here are a few typical methods employed in the procedure:

Data cleaning is necessary because raw data frequently has errors, missing numbers, outliers, or inconsistencies. To resolve these problems and make sure the data is accurate and dependable, data cleaning entails deleting or impute missing items, fixing mistakes, and dealing with outliers.

Data integration: Data integration is the process of combining information from various sources into one coherent dataset. This method entails addressing data format issues, standardising variables, and combining datasets based on shared identifiers.

Data transformation: To make data appropriate for analysis, data must be transformed by changing its structure or representation. To do this, methods like as normalisation, scaling, logarithmic transformations, or the creation of new derived variables are used.

Filtering and subsetting: Filtering and subsetting is the process of choosing particular subsets of data depending on parameters like time periods, regions of the world, or particular features. These methods aid in concentrating the analysis on pertinent data subsets.

Aggregation and Summarization: Aggregation is the process of combining several data points into a single, comprehensive representation, frequently using statistics like mean, median, sum, or count. Techniques for summarising data give a broad picture of the information, facilitating greater comprehension and visualisation.

Changes to the data's structure, often from a wide to a long format or vice versa, are called "data reshaping." Using this method, data can be changed into a format that is better suited for analysis, visualisation, or certain modelling techniques.

Data encoding and standardisation: Standardising numerical variables and encoding categorical data into numerical representations helps enhance analysis and modelling. It is typical to employ methods like one-hot encoding, label encoding, or z-score standardisation.

Feature engineering is the process of developing additional variables or features based on the data already available to increase the predicted accuracy of models. This could entail coming up with polynomial features, time-based features, or interaction terms.

Data Sampling: To obtain representative subsets from a larger dataset, data sampling techniques are used. To alleviate data imbalance or lower computational needs, techniques including random sampling, stratified sampling, or over-/undersampling are used.

Time Series Analysis: Specialised methods must be used to handle temporal interdependence in time series data. To analyse and model time-dependent data, methods like lagged variables, differencing, or seasonal decomposition are used.

These methods are not all-inclusive, and the choice of strategies depends on the particular dataset, the goals of the research, and the analytical tools employed. For the integrity and correctness of the emerging insights, it is crucial to carefully and thoughtfully employ these procedures.

👍Anushree Shinde  [ MBA] 

Business Analyst

10BestInCity.com Venture

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10bestincityanushree@gmail.com

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#DataManipulation , #DataCleaning

#DataIntegration , #DataTransformation

#DataFiltering , #DataAggregation

#DataSummarization , #DataReshaping

#DataEncoding , #FeatureEngineering

#DataSampling , #TimeSeriesAnalysis

#DataInsights , #DataAnalytics

#DataVisualization , #DataPreprocessing

#DataWrangling , #DataAnalysis

#DataMining , #DataScience

Safeguarding Data in the Digital Age: Importance of Data Security and Privacy

 Safeguarding Data in the Digital Age: Importance of Data Security and Privacy

Data is now one of the most valuable resources for people, companies, and governments in the contemporary digital era. The importance of data security has never been greater due to the development of technology and the growing interconnection of our world. Integrity, confidentiality, and accessibility of information in the digital sphere are crucially dependent on data security and privacy. This essay examines the value of data security and privacy while illuminating the difficulties in protecting data and providing recommendations for best practises.

Data security is crucial as businesses and individuals face increasing dangers from hackers, fraudsters, and other bad actors. Data security breaches can have serious repercussions, including monetary losses, reputational harm, and even legal penalties. Data is valuable, which makes it a desirable target for hackers. This includes personal information like social security numbers and credit card information as well as company secrets and government intelligence.

Additionally, data is now accessible from a variety of entry points thanks to cloud computing, Internet of Things (IoT) gadgets, and the growing usage of mobile applications, potentially expanding the risks. Strong data security measures are therefore required to defend against new threats and safeguard private data.

Data privacy refers to a person's right to decide how their personal information is gathered, utilised, and shared. It is strongly related to data security. Data collecting techniques have proliferated in the digital age as businesses collect enormous volumes of personal information to obtain insights, enhance services, and promote targeted advertising.

Personal data misuse or unauthorised access can result in privacy violations, identity theft, and intrusive surveillance. It is critical to establish frameworks and rules that safeguard people's rights to privacy, promote openness and informed consent when managing personal data, and defend those rights. To protect privacy and make businesses responsible for their data handling practises, legal frameworks like the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States have been enacted.

Organisations and people must follow strict data security and privacy practises in order to protect data efficiently. Here are some recommendations for best practises:

Use effective encryption techniques to safeguard data while it is in transit and at rest. Data is encrypted such that it becomes unintelligible and can only be decoded with the right keys, guaranteeing confidentiality even if the data is hacked.

Implement strict access controls and user authentication procedures to restrict access to data to those who are authorised. Unauthorised access can be prevented using multi-factor authentication, strong passwords, and role-based access control.

Regular Auditing and Monitoring: Keep an eye out for any unusual activity or security breaches by regularly auditing and monitoring systems and networks. Quick response and mitigation are made possible by early detection, which lessens the effect of prospective data breaches.

Data minimization means just gathering and keeping the information required for legal purposes. The danger of data loss or tampering is decreased by storing as little data as possible.

Employee Education and Awareness: Inform staff members on recommended practises for data security, such as phishing awareness, password hygiene, and secure browsing. Employees need to understand the potential hazards and how they can help protect data.

Create and put into action a thorough incident response plan to successfully address data breaches. A clear plan guarantees a quick and coordinated response, reducing the potential harm brought on by a breach.

In the digital age, data security and privacy are of the utmost significance. Prioritising the safety of sensitive data is critical as data volume and value keep increasing. Together with privacy policies and regulations, strong data security measures can help reduce risks and ensure that people and organisations keep control over their own data.

👍Anushree Shinde

Anushree  Shinde[ MBA] 

Business Analyst

10BestInCity.com Venture

+91 9011586711

anushree@10bestincity.com

10bestincityanushree@gmail.com

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#DataSecurity , #DataPrivacy . #DigitalAge , #Cybersecurity , #DataProtection #PrivacyMatters , #SecureData , #InformationSecurity , #DigitalPrivacy , #DataSafety #PrivacyRights , #DataBreach , #OnlineSecurity , #ProtectYourData , #SecureDigitalFuture #DataPrivacyAwareness , #DataSafeguarding , #PrivacyProtection , #DataIntegrity #DataConfidentiality , #DataGovernance , #SecureOnlinePresence