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      Retail  experience  and  e-commerce: Retailers  study  customer
                        behaviour,  including  browsing  history  and  previous  purchases,
                        to predict which products or content a customer is most likely to
                        choose.  Sales  data  is  also  connected  with  local factors  such  as
                        weather conditions and events to improve stock control. In addition,

                        prices can be adjusted dynamically based on market demand and
                        competitor pricing.

                                                 Financial experience and banking: Banks and financial institutions
                                                  use data  analytics  to detect  suspicious  transactions  and prevent
                                                  fraud. It also helps in assessing a borrower’s credit risk by analysing
                                                  their past financial records and transaction history.

                       Healthcare  experience  and  problem-solving: Hospitals  analyse

                        patient records, lifestyles and medical histories to predict the risk of
                        diseases. Similarly, AI technology can quickly scan large volumes of
                        medical data to match patients with suitable clinical trials, instead
                        of spending weeks or months on repeated searching. This supports
                        faster research and improved treatment outcomes.

                                                 Social  media and  marketing:  Brands use  sentiment  analysis  to
                                                  examine online reviews and comments in order to understand public
                                                  opinion.  User behaviour  is  closely  monitored  so  that  personalised
                                                  advertisements and content can be delivered, increasing engagement
                                                  and relevance.

                       Tech and  services  for food:  Machine  learning  is  used  by  food
                        delivery  services  to  estimate  preparation  and  delivery  times  by
                        analysing order details, restaurant workload and time of day. This
                        helps improve efficiency and ensures faster, more reliable service for
                        customers.

                                                 Business forecasting: Business  forecasting  uses  data  science  to
                                                  examine  historical  data  and  market  trends,  helping  organisations
                                                  predict future sales, demand and growth, which supports informed

                                                  decision-making,  reduces  risks,  improves  planning  and  guides
                                                  effective long-term business strategies overall.

                                                                                       AI Domains and Applications     15

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