<prompt> Rala mokhoa o thehiloeng ho AI ho lemoha liketso tsa bomenemene litšebelisanong tsa lichelete.nan  <response>: Instruction in English: Design an AI-based process to detect fraudulent activities in financial transactions. . Response in English: Here is an outline of how an AI-based process could be designed to detect fraudulent activities in financial transactions:1. Data collection and preprocessing: Collect and clean large datasets of financial transactions from various sources. This data would include information about the date and time of transactions, the amount, the payee and payer identities, among others. 2. Feature engineering: Extract relevant features from the data that might be indicative of fraudulent behavior, such as irregular transaction patterns or sudden increases in transaction amounts.3. Machine learning model: Develop and train a machine learning model, such as a neural network or decision tree, on this data. The model would use historical data to learn how to predict whether a given transaction is likely to be fraudulent.4. Model deployment: Integrate the model into the financial transaction processing pipeline. The model would analyze each transaction in real-time and assign it a probability of being fraudulent.5. Threshold setting: Set a threshold value for the probability of fraud above which a transaction would be flagged for further investigation. This threshold could be adjusted based on the desired balance between false positives and missed frauds.6. Alerts and investigations: Send alerts to the appropriate personnel whenever a transaction is flagged as potentially fraudulent. Conduct further investigation to determine whether or not the transaction is indeed fraudulent.7. Feedback loop: Continuously update the model with new data, including feedback from investigations, to improve its accuracy over time.This AI-based process would allow for fast, automated detection of potentially fraudulent activities in financial transactions, enabling organizations to respond quickly and prevent financial losses. . Response in Sesotho: Mona ke setšoantšo sa hore na mokhoa o thehiloeng ho AI o ka etsoa joang ho lemoha liketso tsa bolotsana litabeng tsa lichelete: 1. Pokello ea lintlha le ho e lokisa esale pele: Bokella le ho hloekisa li-dataset tse kholo tsa lichelete ho tsoa mehloling e fapaneng. Lintlha tsena li tla kenyelletsa lintlha tse mabapi le letsatsi le nako ea thekiso, chelete, boitsebahatso ba motho ea lefuoang le molefi, hara tse ling. 2. Boenjineri ba likarolo: Ntša likarolo tse amehang ho data tse ka bang tse bontšang boitšoaro ba bomenemene, joalo ka mekhoa e sa tloaelehang ea teraka kapa keketseho ea tšohanyetso ea litefiso tsa teraka. 3. Mohlala oa ho ithuta ka mochini: Theha le ho koetlisa mohlala oa ho ithuta ka mochini, joalo ka marang-rang a methapo kapa sefate sa liqeto, ho data ena. Moetso ona o tla sebelisa lintlha tsa nalane ho ithuta ho bolela esale pele hore na khoebo e fanoeng e kanna ea ba ea bomenemene. 4. Tšebeliso ea mohlala: Ho kopanya mohlala ho phaephe ea tsebetso ea litšebelisano tsa lichelete. Moetso o ne o tla sekaseka khoebo e ngoe le e ngoe ka nako ea nnete mme e fane ka monyetla oa ho ba bomenemene. 5. Sethapo sa Motheo: Beha moeli oa boleng bakeng sa monyetla oa bomenemene ka holimo moo khoebo e tla tšoauoa bakeng sa lipatlisiso tse ling. Moeli ona o ka fetoloa ho ipapisitsoe le tekanyo e batloang lipakeng tsa bomenemene le bomenemene bo fositseng. 6. Litlhokomeliso le lipatlisiso: Romela litemoso ho basebetsi ba loketseng neng le neng ha khoebo e tšoauoa e le e ka bang bolotsana. Etsa liphuputso tse ling ho fumana hore na khoebo e hlile e entsoe ka bolotsana kapa che. 7. Feedback loop: Tsoela pele ho nchafatsa mohlala ka lintlha tse ncha, ho kenyelletsa le maikutlo a tsoang lipatlisisong, ho ntlafatsa ho nepahala ha nako. Tsebetso ena e thehiloeng ho AI e ne e tla lumella ho lemoha ka potlako, ka mokhoa o ikemetseng oa mesebetsi e ka nang ea e-ba bolotsana litabeng tsa lichelete, ho nolofalletsa mekhatlo ho arabela ka potlako le ho thibela tahlehelo ea lichelete. 