Computational Intelligence for Modern Business Systems

Machine learning is coming as a significant encroachment in the financial services industry. Finance has always been about data and is considered a complex field of study that includes knowledge from disciplines such as mathematics and statistics to human psychology and linguistics. Due to this, it is difficult to manage the various day-to-day challenges associated with finance, such as financial glitches attributed to human errors. The financial sector has employed machine learning for a myriad of purposes and its excellent applications. The work highlights the advancement of different learning techniques in financial services for data science. This chapter gives comprehensive prospects on a study accomplished in the financial industry after applying digital financial solutions over time and an extensive view of this distinctive research area. The organization comprises an introduction, motivation, and background that entails a block diagram of learning techniques, benefits, and various issues related to implementing machine learning techniques in the financial domain. Other things covered in this chapter are various datasets used by different researchers, and its focal point is to present a systematic survey of various applications of finance using artificial intelligence and finally expose a synthesis analysis based on the findings along with their benefits and issues. Overall, this chapter gives conscious and constructive assistance to researchers working towards the sustainable evolution of the finance industry.

Due to the sudden outbreak of novel Coronavirus (Covid-19), the world economy came to an abrupt standstill. The financial market almost collapsed during quarantine, workers were left jobless and many companies ran bankrupt. One of the methods for investigating the impact of Covid-19 on financial market is by analysing the stock market. The daily fluctuations of stock prices help the investors to get an insight about the overall stock market. Hence, in order to study the impact of Covid-19 on financial market, a comprehensive comparison of the stock market pre-Covid-19, during Covid-19 and post-Covid-19 for tech giants like Google, Apple, General Electric, IBM and Microsoft has been implemented. The dataset has been imported using the Yahoo Finance API (Application Programming Interface).

The real estate market shows huge behavioural dispositions recorded in the customary financial markets. The principal goal of this examination is to recognize the Behavioral Factors that impact the assessment of investment of investors in the realty market. The primary target of this examination is to characterize the feelings-based hypotheses utilized to clarify the financial exchange issues and terms. In this paper, it is realized that feelings can’t generally spur investors, and it isn’t vital that the property market effectively be adequate at the feeble structure. There is a need for a profound examination of the hypothesis of behavioural account. This investigation is helpful to comprehend the investments by utilizing the behavioural model. Using digital and statical analysis using ML techniques get a chance to improve Behavioral Research on The Analysis of Investment Decisions in The Commercial Realty Market and further analyse the stock. Investors consistently need to put resources into those tasks with more prominent benefits and the capital’s base odds of risk or loss.

The research work is focused on examining the role of artificial intelligence (AI) in addressing challenges associated with money laundering in the banking sector. Money laundering is a global issue that threatens financial stability and international security, making anti-money laundering research crucial. Furthermore, just 0.2% of money laundered through the financial system is estimated to be seized. The crime is growing increasingly sophisticated and intricate, and the amount of the crime increases banks’ vulnerability. Researchers have begun to investigate the possibility of artificial intelligence approaches in this setting. However, a thorough assessment has identified a systematic knowledge deficit that systematically examines and synthesizes artificial intelligence techniques for anti-money laundering efforts in the banking industry. Therefore, this chapter is focused on a systematic review of key technologies categorized into artificial intelligence or machine learning (AI or ML), natural language processing (NLP), robotic process automation (RPA), and cloud-based solutions. However, various challenges concerned with these techniques, such as data quality, the nature of money laundering and data volume, and data heterogeneity, are also discussed. As a result, the findings add to the total knowledge base in anti-money laundering from the banking sector’s perspective. Additionally, future study directions were narrowed even further based on the limitations discovered.

Crowdfunding has become the social media version of fundraising campaigns whose underlying principle is to raise funds for a project from multiple people and to collectively accrue the required resources to make the project successful. The principal aim of crowdfunding platforms is to introduce budding entrepreneurs to an expanded pool of investors rather than traditional financial investors. Kickstarter is the largest reward-based crowdfunding platform which has successfully funded more than 2,00,000 projects and raised more than $6 billion. However, scarcely one-third of the projects are successful in reaching the funding goal before the deadline. Hence, reckoning the probability of success of a project is an interesting challenge. The proposed system helps classify a project as a success or failure. Supervised Machine Learning Models are implemented from which Random Forest provides the highest accuracy score of 90%. Regression Algorithms are implemented to estimate the funding a project is capable of achieving. Furthermore, BERT, spaCy, and TF-IDF are implemented to find keywords that affect the success of the project.

The stock market has an important role in the development of modern society. They allow the deployment of economic resources. Changes in stock prices reflect changes in the market. With powerful data processing capabilities in many fields, deep learning is also widely used in the financial field such as: stock market prediction, optimal investment, financial information processing, and execute financial trading strategies. Therefore, stock market prediction is considered one of the most popular and valuable areas in the financial sector. In this study, we propose using multi deep learning algorithms for stock prediction: RNN, LSTM, CNN, and BiLSTM. We do experiments on a stock that has a wide range of trading days and use them to predict daily closing prices. The experimental results show that the multi deep learning models can achieve good results in predicting stock prices compared to many traditional prediction models.

Depicting the price of a home is becoming crucial day by day, as the cost of land and houses rises year after year. House prices are significantly associated with features such as locality, region, house, age, and people; forecasting price of an individual house needs a lot of information. The House Price Index (HPI) is one of the standard tools for assessing house price variations. The objective of the study is to predict the price of houses with the use of various regression approaches of supervised machine learning. In the proposed study, housing data of 5000 homes in USA have been analyzed. Six models are used, namely, Random Forest Regression, XGBoost Regression, Linear Regression, Artificial Neural Network, Gradient Boosting and Ada Boosting. The results are compared, of which Linear Regression and Artificial Neural Network outperformed with R2_Score of 91.56 and 91.37% compared to the other four algorithms.

In recent years there has been a growing demand for network resources. However, fixed contracts between users and providers tend to result in network use inefficiencies and high costs. To promote the best accommodation for high network demand and usage, a setup where every user has the most amount of network resources at his disposal is paramount—this way users minimize the risk of not having sufficient resources to meet their service needs, and providers maximize the usage of their networks. In this chapter, we consider a setup based on Software Defined Networking (SDN), where connections between users’ devices and providers’ nodes are defined according to resource needs and pricing. The adoption of an SDN-based approach is detrimental of other more distributed control alternatives is since the scenario under investigation is very specific and dynamic, which is more efficiently managed in a logical centralized way than in a decentralized way. In this direction, an auction SDN-based broker is proposed, so that both users and providers get the best deal for every resource-allocation procedure, according to all players’ needs and network restrictions. We present and discuss evaluation results taken from our auction business model. Our results suggest that the best bidding strategy depends on several aspects, namely: (i) the competitor’s bidding strategy; (ii) the operating cost of each participant; or (iii) the available resources of all participants and the broker’s requisites.

Technological advancements have made the most disruptive change in marketing and consumer behavior in the last few decades. The history of change suggests that technology has entirely transformed the media from cable TV to more personalized technologies. In particular, the Internet and other relevant information technologies and platforms such as social media, powerful search engine, big data, mobile apps, and augmented reality are redefining marketing theories and practices. These advancements, on the one hand, have enabled marketers to enhance customer relationship and engagement. On the other hand, customers are becoming more powerful than sellers in creating and controlling the information content. Artificial intelligence (AI), the use of computerized programs and machinery that exhibit human intelligence, is expected to have even much greater impact on marketing and customer behavior than social media and other recent advancements. Applying a desk research method, the primary purpose of this chapter is to highlight the present state of the application of AI in marketing with a focus on digital marketing. The chapter also aims to identify the future directions of digital marketing with AI as a potential major driver. The major hotspots identified for future research include future marketing jobs and relevant skills, change in consumer decision making, AI-driven social media marketing and new product development, enhanced recommender engine, and augmented reality marketing. Practically, the findings will help marketers better prepare for designing marketing strategies for ever-emerging and more empowered digital consumers.

This chapter examines the World Book Fair (WBF) organised in New Delhi by the National Book Trust (NBT), India, a public sector entity. The World Book Fair was initially set up as a biennial event but was changed into an annual event from 2012 onwards in an announcement by the Cabinet Minister concerned. The case takes through the challenges that the then NBT Director and his team faced in making this change and outlines how they tackled these challenges. The case study aims to take through an example of how to bring about changes in the functioning of the public sector, with its inherent constraints in terms of resources, bureaucratic inertia, lack of coordination and competing interests. This case study broadly sets to examine the significant presence of the public sector across the world and its dominance in some parts of the world.

Companies are constantly looking for methods to keep their workers with them to minimize further recruitment and training expenses. Predicting whether a specific staff member can depart helps the business to take preventative measures. Unlike physical systems, a scientific and analytical formula cannot explain human resource issues. Machine learning methods are thus the ideal instruments for this purpose. This chapter offers a three-stage paradigm for the prevention of attrition (up to processing, processing, post-processing). IBM HR dataset for the case study is selected. As there are many functions in the dataset, the selection technique for the “maximum-out” feature is suggested for the extent of decreasing the to-processing phase. In the planning retrogression model, the coefficient of each variable indicates the significance of the feature in attrition predictions. The findings indicate an improvement in the F1 score using the “maximum-out” feature selection technique. Finally, through learning the model for many bootstrap datasets, the validity of data is verified. The average deviation of the parameters is then evaluated to verify the confidence and stability of the model parameters. The modest average deviation of the data shows the model is stable and generalized.

Customer relationship management (CRM) is a set of technologies, methods, and practices that companies adopt to analyze and manage customer interactions and data throughout their engagement with them. The primary purpose behind using CRM is to enhance the business and improve interaction with the customer at the service point. It also helps in increasing the revenue of suppliers or manufacturers and is more useful in customer retention. The efficient operation of the CRM system needs to collect information from several contact points with the consumer, such as direct phone calls, live chat, marketing materials, the company’s website, chatbot, social media, and mail communication. In addition, the CRM system also collects and provides detailed information about the customers’ personal information, preferences, history of purchase, and various concerns with the employees from the marketing side. Thus, the CRM system rapidly processes a huge volume of data from the consumer and uploads numerous data from the supplier or manufacturer. In such cases, an efficient way of handling this voluminous data is highly needed. The advent of Data Mining techniques and their rapid growth makes it easier to handle this large volume of CRM data more effectively. The present work discusses various Data Mining techniques and their application to CRM. It also outlines how effective it is to make a contemporary CRM from a conventional one.

Employees are viewed as an asset that leads to an organization’s growth. As competition grows in the market, the attrition of employees increases. Consequently, employee attrition can be a major problem for businesses, particularly when there is mobility or movement of technically skilled employees, in search of better opportunity. This results in financial loss for a company or organization. Therefore, in this chapter, advantage of machine learning algorithm is presented to predict the behavior of current employees’ employees. In this research, Long Short-Term Memory (LSTM) algorithm and fuzzy rules are used to analyzes attrition actions of workers by forecasting a shortage of qualified employees by department and sending out a warning message about recruiting new employees or distributing the workload among existing employees. This template also ensures that incentive and promotion decisions are made without bias, assisting the organization’s growth indirectly.

Nowadays, Culture shock is very much common to work life and this is an important issue in identification of the variables to develop morale of the employees towards an aim to increase productivity. The term ‘cultural shock’ refers the emotional state of uncertainty, confusion, anxiety that people may experience when transforming to a new state of affairs or feeling a new work culture or social status. So, it happens when an individual is censored from acquainted surroundings and culture after transferring to a new environment. It brings the logic that Culture shock tends to mean a bustle of emotions, including excitement, emotional labor, job stress, and job satisfaction and helplessness. Social scientists are of the opinion that culture shock is treated as ‘mental illness’ as common men are suffering as they are distracted from the cultural environment. In this context, it is pertinent to identify the impact of cultural shock during the pandemic time especially in the service sector management as people are constrained to work from home and that is also to some extent detrimental to their mental health. In this present study, the researchers are trying to explore the possible attributes that are responsible for cultural shock and also try to measure the possible impact of the same. This study will provide a working model for the stakeholders to frame a strategy to get rid of the crisis and develop an employee retention policy with respect to stress coping behavior as well, in the days of New Normal. The study is essentially focused on tech-based gaming industry. The present chapter proposes a new q-Rung Orthopair Fuzzy (qROF) based computational intelligence framework of psychological assessment. The procedural steps of forced field analysis (FFA) is followed in this chapter wherein Level Based Weight Assessment (LBWA) is applied for calculating the weights of the attributes. Further, it carries out stability analysis of the results obtained. Finally, it puts forth policy recommendations for formulating effective employee motivation and retention strategy.

Personality is an important factor for predicting whether an applicant would be a perfect fit for the company. The personality of a candidate can give the recruiters an insight about the candidate and hence it can improve candidate selection. The fate of an organization depends on its employees, which makes the selection of the best candidate a very crucial matter for an organization. In the current scenario, the applicant will be selected for the particular job by going through his/her Curriculum Vitae (CV). But shortlisting and going through thousands of applicant CVs is a tedious and hectic task. Besides, one may not get a good idea about the personality of the candidate from a CV. The cost of bad hires can be saved if the recruiter selects the right candidate for a particular job role. Personality Prediction is finding out and comprehending the personality of an applicant which can be used in the present recruiting system. The personality of the candidate will not only help the recruiters in the selection but also provide the candidate with a good job role based on his personality. In this chapter, we have come up with a method to evaluate the personality of a candidate using different strategies. The proposed system asks CV-related and personality-based questions to predict and analyze his/her personality with the help of Machine Learning and Natural Language Processing which helps the organization to shortlist candidates based on the job profile and company requirements. Various Machine Learning Models were tested from which Logistic Regression provided the highest accuracy of 85.71%. Bidirectional Encoder Representations from Transformers or BERT is implemented to extract the keywords to provide recruiters an understanding about the candidate’s personality. Thus, the system will help the human resource to select the right candidate for the desired job profile, which in turn will provide an expert workforce for the organization.

The business of a company relies on the delivery of quality products and services with optimal resource usage and it has been a constant endeavor to form timely strategies for improvements in operational efficiency. Many a time product development companies can find opportunities to cut down on repetitive and labour-intensive business processing tasks. Automating the routine process can be a wise business strategy to improve operational efficiency. In this direction, the usage of artificial intelligence concepts like machine learning, deep learning, and reinforcement learning in software product development has been the top choice of many business firms. We believe in, developing value-added differentiators based on recent technological advances in large data management, image processing, and AI/ML algorithm technologies to speed up the drive towards operational excellence. These technologies are at an inflection point, have never been seen before, and definitely can aid in further advancement of business strategies. In this chapter, we discuss one such tool that uses advanced image processing and deep learning algorithms to segment the failure regions from disintegrated automobile assembly parts images. Image segmentation is an aspect of image processing that finds its vast applications in industries and with the advent of machine learning techniques, segmentation has become handier in terms of its computational efficiency. In our technical approach, we use fully convolutional neural networks to segment the region of interest (failure regions) from the image obtained after disintegrating the automobile part, specifically the engine DNox Supply module. One interesting aspect of this work was making segmentation achieve an accuracy of 87% for validation and 98% for training with the sparse dataset. The proposed methodology helped by bringing in intelligent automation instead of manual intensive activity for identifying the region of interest around the failures or abrasions seen in the assembly parts. The generated business reports are shared with the OEM (Original Equipment Manufacturers) for further improvements in the quality of the parts. As illustrated, we can bring in around 40–50% productivity gains along with upwards of 30% cost reduction.

Industrial 4.0 (4th industrial revolution) embodies rising technological advances withinside the improvement of clever manufacturing strategies. Industry 4.0 has great potential for many manufacturing companies to allow customization of products, provide flexibility to meet new needs in real-time and produce highly efficient jobs. The 4th Industrial Revolution and rising technologies—which include the Internet of Things, artificial intelligence, robots, and greater productions—affect the emergence of the latest manufacturing techniques and commercial enterprise fashions that transform fundamental manufacturing. The next generation of our industry is Industry 4.0—with the guarantee of improved production flexibility, as well as greater customization, improved productivity, and better quality. This allows companies to access additional products designed for each in a shorter period of time and better market standards. Intelligent manufacturing performs a critical position in Industry 4.0. Ordinary tools are transformed into intelligent objects that you will hear, handle, and perform in an intelligent environment. This makes industrial autonomy a reality where different countries work together to develop technology and start the next generation which is a 5.0 industrial revolution. Here in our work, we’ve mentioned the effect of Industry 4.0 on making the arena digital. Industry 4.0 is ubiquitous; however, we goal to take a more in-depth study of the cutting-edge enterprise imaginative and prescient and display the enterprise 4.0 destiny trends. Additionally, this chapter introduces generation to transport from Industry 4.0 to Society 5.0 and anticipates the destiny from Industry 4.0 to Industry 5.0.

The key premise of smart factories and enterprise 4.0 is the application of AI by employing robots to perform hard activities, lower fees, and improve the high-quality of products and solutions. Artificial intelligence (AI) is infiltrating the industrial sector with the help of cyber-bodily systems, fusing the physical and digital worlds. Artificial intelligence (AI) makes manufacturing smarter and more capable of coping with modern difficulties like customizable needs, faster time to market, and an expanding spectrum of sensors in equipment. The usage of bendy robots combined with artificial intelligence facilitates the production of a wide range of products. AI technologies can be used to analyse massive volumes of real-time data collected from a variety of sensors (such as data mining). AI is ushering in a new industrial revolution with intelligent automation, massive data, and networking. Time or place, data integration universally with networks evolves and allows completely automated supply chains, Industry 4.0 will bring the integration of horizontal and vertical systems with businesses, departments, features, and talents will become much more cohesive. Extra systems will be enhanced with embedded computers as the Internet of Things becomes more industrialized, and they will be connected using standard technologies. This allows machines to communicate and interact with one another, and a more centralized machine controller becomes increasingly vital. As cross-company, universal data-integration networks expand and enable totally automated value chains in Industry 4.0, horizontal and vertical system integration among firms, departments, functions, and capacities will become much more cohesive. Industrial auto solutions and the Internet of Things will also add embedded computing to more objects and connect them using standard standards.

The purpose of this research is to develop a trustworthy decision-making tool that integrates the thinking process, the concept of six sigma, and lean thinking in a long-term supply chain. Supplier quality was recognized as a supply chain barrier in this study because ecologically friendly materials result in a sustainable supply chain. The purpose of this study is to fill a vacuum in the existing literature by emphasizing the connections between theory of constraints, six sigma, and lean thinking for continuous improvement. This study combines the notion of constraints, six sigma, and lean thinking into one evaluation model. Before selecting the best provider with the least amount of environmental impact, the programme looks at a variety of qualitative and quantitative parameters. Supplier 1 appears to be the best, followed by the others. The sensitivity analysis of the model assumes that “higher is better,” and the model's output identifies the best supplier with the least environmental impact.

Artificial Intelligence or machine intelligence is a combination of multiple types of technologies which work in tandem to help machines to sense their surroundings, understand complex problems, provide solutions and learn new things mirroring a human-like level of intelligence. With the increasing capabilities and sophistication of AI systems, it is now being used in multiple ranges of sectors including pharmaceuticals, finance, transport, energy, public services, cybersecurity, and automotive. In the last few years Artificial Intelligence is said to have self-evolved, software has been created by researchers that borrow concepts from Darwinian evolution, and its usage is resulting in better efficiency, lesser time to complete tasks, and higher accuracy in workloads. The latest 2020 global projects by Artificial Intelligence include it in the health sector to help diagnose and treat COVID-19, to spot brain tumors, etc.; in Technology and Cyber Security to spot critical Microsoft security bug, to distinguish between bots and humans, quantum information processing, etc. AI in automation to reduce traffic congestion and fuel consumption is an example of AI not only helping in making our life easier and fast but also helping the environment. The upcoming stage of Artificial Intelligence is the era of Augmented Intelligence, which is going to link humans and machines seamlessly together.

Choosing the optimal route in transport is a daily challenge for all companies in the world that deal with the transport of goods. Routing is the selection of the optimal path in the network of roads on which vehicles should move, i.e., defining the most appropriate and fastest mode of transport from the starting point to the endpoint. X express company in Bosnia and Herzegovina, with 16 distribution centers, currently has over 350 delivery vehicles equipped with GPS devices. Drivers of vehicles are faced daily with the selection of the optimal route so that all shipments are delivered as soon as possible, with minimal transport costs and so that all customer requirements are met. The most commonly used algorithm for solving vehicle routing problems is the Savings Algorithm or the Clark and Wright algorithm. Along with the use of the Clark-Wright algorithm, this chapter presents the routing of a delivery vehicle with predefined locations for the shipments in the delivery area of the distribution center Banja Luka. For all 27 delivery locations, the distance from the central warehouse and possible savings were defined, after which the connection was made and the final optimal route was determined. The obtained results were compared with the actual mileage of the delivery vehicle, where the functionality and significance of the Clark-Wright algorithm were proven.

Covid-19 is a curse to the people of this century and there is no such thing as a plague. If someone is infected with Covid-19, we must follow the doctor's instructions as to which foods should be eaten more frequently at that time. We attempt to discuss this in details. The objective of this chapter is to see how a country’s food affects its Covid-19 mortality rate. With so many diverse eating cultures throughout the world, it’d be fascinating to examine which food groups can best predict a country’s death rate. We used a machine learning model (Linear Regression and Random Forest/Regression Tree) to estimate the proportion of fatalities caused by the coronavirus pandemic, taking into consideration statistical data on the population’s eating habits (food types: animal, eggs, fish, beer, etc.). We may deduce which sorts of meals have a greater influence on the ultimate outcome based on the model’s predictions. Furthermore, throughout the Covid-19 period, the economic environment has changed considerably. In this chapter, we examined restaurant meals in a variety of ways.

The current state-of-the-art density map-based crowd counting methods have focused on designing convolution neural network (CNN)-based models to exploit multiscale features to handle crowd shape change due to perspective distortion. However, the significant concerns with such approaches are using static kernels and being not adaptive to input data. Again, the multiscale features should be more attentive towards background minimization. Hence, this chapter proposes a de-background multicolumn dynamic CNN for crowd counting to address the issues. The proposed model can handle crowd shape change due to perspective distortion and learn to minimize the background influence while doing crowd counting. Two benchmark crowd counting datasets, Mall and UCSD, are used to show the model’s effectiveness. In addition to this, a separate ablation study has been conducted to show the effect of individual modules of the proposed model.

The advancement in technology has enabled sellers to discriminate based on customer-revealed purchasing intention. Sellers can now track the things purchased by buyers using various new technologies like sensors and RFID tags, and with this, there are new challenges in the implementation of bundling. To use this data, see what this new perceivability means for evaluating and market results. A detailed critical review was carried out on product bundling and their practices across the world in different markets. On the basis of our review of literature, it can be concluded that there are certain factors that are significantly more important than others. In this study, observations are made on how different factors compare to each other, and which should be prioritized. Information quality for the relevant bundling as per the market requirements is the most critical factor and transportation, while significant, is the least important.

The chapter is more emphatic on the possibility of developing statistical techniques coping with the competition science to create a powerful decision making tool appropriate for sustainable supply chain network analysis. It highlights two independent techniques, to improve the quality of the outcomes in the decision making process. The former one focuses on the deployment of natural risk involved in the network. The concept of conditional probability is assigned to the conventional deterministic adjacency matrix in order to figure the uncertain interactions in the network. This will be useful to identify the crucial nodes in any supply chain and advantageous to classify these nodes based on their natural risk factor. The model concentrates on the delay time deviations from the mean delay time as the main competition. The combination of the two methodologies will find their position in improving the quality of the decision making actively.

Any firm’s sustainability performance can be enhanced through effective implementation of supply-chain and reverse logistics strategies. Although the reverse logistics concepts have been recognized to be very important in developing countries, however, the companies’ progress has been hampered owing to certain prevailing variables responsible for their sustainable developments. Thus, this study focused on identifying the associated variable(s) with reverse logistics practices in the bottle manufacturing companies in Odisha (India), and to rank those variables by using the technique for order of preference by similarity to ideal solution (TOPSIS) method. These research findings will provide a path to the professionals and other decision-makers in planning for suitable strategies in the reverse logistics-based performances in the manufacturing sectors.

Governments worldwide are exploring the applications of artificial intelligence (AI) technologies to improve public services. Organizations may utilize AI, machine learning, deep learning, and big data together to acquire relevant business insights, increase efficiencies, make better decisions, and advance their objectives. Governments worldwide are rapidly developing and deploying artificial intelligence derived from machine learning to improve operations, public services, compliance, and security activities. Government procurement in Nigeria has had several issues, including corruption. Corruption from both government officials and contractors has been an issue of significant concern. This chapter looks at how several government agencies in developed countries around the world are using artificial intelligence (AI) in their contracting procedures and some of the capabilities that are being considered for future use. This study looks at ways in which the Nigerian government could implement AI in its contracting to maximize efficiency and reduce the rate of corruption. AI has the potential to profoundly change how government agencies handle contracting, from vendor selection through contract closeout and everything in between. However, AI is not error-proof, and issues regarding technology implementation are also reviewed in this chapter.