Features of KTern's business function analytics.pdf

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KTern.AI is an AI tool that automates the analysis of business functions by classifying them as active or inactive, determining their compatibility with SAP S/4HANA as either enterprise extensions, enterprise business functions, or industry business functions, and allowing users to search and sort functions according to status, compatibility, and type.

KTern.AI
Business
Function
Analytics
SWIPE
Featuresof
@KTern.AI

Automated Business
Function Analysis
KTern.AI automates the process of
analysing business functions.
01
@KTern.AI

Active/Inactive
Classification
Business functions are classified into
active and inactive categories.
02
@KTern.AI

Compatibility
Analysis
Business functions are also classified
into Enterprise Extensions, Enterprise
Business Functions, and Industry
Business Functions.
03
@KTern.AI

Industry-Specific
Classification
KTern.AI determines the compatibility
of business functions with S/4HANA.
04
@KTern.AI

Search/Sort
Functionality
Business functions can be sorted and
searched according to status,
compatibility, and type.
05
@KTern.AI

Recap
Automated Business Function Analysis
Active/Inactive Classification
Compatibility Analysis
Industry-Specific Classification
Search/Sort Functionality
@KTern.AI

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In our research paper titled "Qualitative Analysis on Tax Evasion and Reasons for Reluctance to Join ATL in Pakistan," we delve into the intricate dynamics of tax compliance and explore the underlying reasons why individuals in Pakistan might hesitate to become a part of the Active Taxpayer List (ATL) within the framework of business law and taxation. With a multidisciplinary approach, our team of five researchers embarked on an in-depth exploration of tax evasion behaviors and the factors influencing them. Through qualitative analysis, we aimed to unravel the complex interplay of socio-economic, cultural, and institutional factors shaping tax compliance attitudes among Pakistani taxpayers. Drawing on a diverse range of qualitative research methods, including interviews, focus groups, and content analysis, we gathered rich insights from a diverse sample of individuals representing various sectors of the economy. By immersing ourselves in the lived experiences and perceptions of taxpayers, tax advisors, and legal experts, we uncovered nuanced perspectives on tax evasion practices and attitudes toward ATL membership. Our findings highlight the multifaceted nature of tax evasion in Pakistan, shedding light on both the motivations driving non-compliance and the barriers to ATL enrollment. From perceived inefficiencies in the tax system to cultural norms and trust deficits in governmental institutions, our research identifies a myriad of factors contributing to tax evasion behavior and reluctance to join ATL. Furthermore, our study offers valuable implications for policymakers, tax authorities, and business stakeholders seeking to enhance tax compliance and foster a culture of transparency and accountability in Pakistan's taxation landscape. By addressing the underlying drivers of tax evasion and promoting the benefits of ATL membership, policymakers can design more effective strategies to broaden the tax base and promote sustainable economic development. In conclusion, our research paper represents a comprehensive endeavor to deepen our understanding of tax compliance dynamics in Pakistan and elucidate the complexities surrounding ATL enrollment. By unraveling the intricacies of tax evasion behaviors and attitudes, we hope to contribute to informed policymaking and foster a culture of tax compliance and integrity in Pakistan's business and taxation environment.

Challenges and Opportunities: A Qualitative Study on Tax Compliance in Pakistan

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