@Article{crph1297,
AUTHOR = {Adusupalli, Balaji and Singireddy, Sneha and Sriram, Harish Kumar and Kaulwar, Pallav Kumar and Malempati, Murali},
TITLE = {Revolutionizing Risk Assessment and Financial Ecosystems with Smart Automation, Secure Digital Solutions, and Advanced Analytical Frameworks},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2021},
NUMBER = {1},
PAGES = {101-122},
URL = {https://www.scipublications.com/journal/index.php/UJFE/article/view/1297},
ISSN = {2831-5162},
DOI = {10.31586/ujfe.2021.1297},
ABSTRACT = {For years, risk assessment and financial calculations have been based on mathematical, statistical, and actuarial studies of existing and historical data. The manual process of building datasets, processing data, deriving trends, identifying periodicities, and analyzing diagnostics is extremely expensive and time-consuming. With the automation and evolution of data science technologies, organizations are now bringing in niche data, such as unstructured data, which contain more disruptive and precise signals for decision-making—thereby making predictions and derivative valuations more robust. This discussion highlights how investment decision-making and financial ecosystem activities are set to be transformed with the power of technical automation, data, and artificial intelligence. A noted trend in the financial investment sector is that financial valuations are highly predictive and highly non-linear in long-term occurrences. To understand these robust evolving signals and execute profitable strategies upon them, the investment management process needs to be very dynamic, open, smart, and technically deep. However, with current manual processes, reaching a high-end asset prediction still seems like a shot in the dark. In parallel, open and democratically developed financial ecosystems query relatively riskless premium opportunities in high-finance valuation and perception. The process of evolving financial ecosystems or the use of automated tools and data to move to unique frontiers could make high-yield profiting opportunities very safe and entirely riskless. Financial economic theories and realistic approximation models support this.},
}

%0 Journal Article
%A Adusupalli, Balaji
%A Singireddy, Sneha
%A Sriram, Harish Kumar
%A Kaulwar, Pallav Kumar
%A Malempati, Murali
%D 2021
%J Current Research in Public Health

%@ 2831-5162
%V 1
%N 1
%P 101-122

%T Revolutionizing Risk Assessment and Financial Ecosystems with Smart Automation, Secure Digital Solutions, and Advanced Analytical Frameworks
%M doi:10.31586/ujfe.2021.1297
%U https://www.scipublications.com/journal/index.php/UJFE/article/view/1297

TY  - JOUR
AU  - Adusupalli, Balaji
AU  - Singireddy, Sneha
AU  - Sriram, Harish Kumar
AU  - Kaulwar, Pallav Kumar
AU  - Malempati, Murali
TI  - Revolutionizing Risk Assessment and Financial Ecosystems with Smart Automation, Secure Digital Solutions, and Advanced Analytical Frameworks
T2  - Current Research in Public Health
PY  - 2021
VL  - 1
IS  - 1
SN  - 2831-5162
SP  - 101
EP  - 122
UR  - https://www.scipublications.com/journal/index.php/UJFE/article/view/1297
AB  - For years, risk assessment and financial calculations have been based on mathematical, statistical, and actuarial studies of existing and historical data. The manual process of building datasets, processing data, deriving trends, identifying periodicities, and analyzing diagnostics is extremely expensive and time-consuming. With the automation and evolution of data science technologies, organizations are now bringing in niche data, such as unstructured data, which contain more disruptive and precise signals for decision-making—thereby making predictions and derivative valuations more robust. This discussion highlights how investment decision-making and financial ecosystem activities are set to be transformed with the power of technical automation, data, and artificial intelligence. A noted trend in the financial investment sector is that financial valuations are highly predictive and highly non-linear in long-term occurrences. To understand these robust evolving signals and execute profitable strategies upon them, the investment management process needs to be very dynamic, open, smart, and technically deep. However, with current manual processes, reaching a high-end asset prediction still seems like a shot in the dark. In parallel, open and democratically developed financial ecosystems query relatively riskless premium opportunities in high-finance valuation and perception. The process of evolving financial ecosystems or the use of automated tools and data to move to unique frontiers could make high-yield profiting opportunities very safe and entirely riskless. Financial economic theories and realistic approximation models support this.
DO  - Revolutionizing Risk Assessment and Financial Ecosystems with Smart Automation, Secure Digital Solutions, and Advanced Analytical Frameworks
TI  - 10.31586/ujfe.2021.1297
ER  -