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Purpose: The purpose of this study is to identify the elements that can enhance financial inclusion (FI) in a nation, which in turn promotes economic development and growth.

Need for the Study: FI is crucial in providing people with the skills and resources to manage their money effectively and make informed financial decisions. Accessible, reliable and secure financial services play a significant role in achieving sustainable development goals (SDGs) and fostering economic progress.

Methodology: Data from 571 respondents were collected for analysis. The study utilises Statistical Package for Social Sciences SPSS and Analysis of Moment Structures AMOS software to analyse data and achieve the study’s objectives. The researchers employ these tools to obtain substantial results.

Findings: The findings indicate that FI contributes to economic growth (84%) and helps in accomplishing SDGs. Access, usage, affordability, technology, availability and technology adoption all play a vital role in increasing FI in the nation.

Practical Implications: The study’s outcomes have practical implications for policymakers and stakeholders, emphasising the importance of promoting FI through various measures such as enhancing access, affordability and technological advancements in financial services.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

The global financial crisis (GFC) of 2007–2008 had far-reaching consequences for the global economy, triggering widespread economic turmoil. We use the event-study method to investigate whether investors priced the effect of significant events before the Lehman Brothers' bankruptcy in European and Asia-Pacific banks. Abnormal returns on the event days range from −4.32% to 5.03% in Europe and −5.13% to 6.57% in Asia-Pacific countries. When Lehman Brothers went bankrupt on September 15, 2008, abnormal returns averaged the lowest at −4.32% in Europe and −5.13% in Asia-Pacific countries. The significant abnormal returns show that Lehman Brothers' collapse was a turning point, and investors paid attention to the precrisis events as warning signs of the oncoming crisis.