<prompt> Hlalosa mohlala oa tsebeliso ea lefatse ea nnete ea theknoloji e fanoeng.Theknoloji: Ho Ithuta ka Mechini  <response>: Instruction in English: Describe an example of a real-world application of the given technology. . Response in English: Machine learning is a rapidly advancing field that has numerous practical applications in the real world. One prominent example is how machine learning is being utilized to dramatically enhance fraud detection in the finance industry. In the past, fraud detection primarily consisted of a set of rule-based systems, which were limited in their ability to adapt to new, emerging fraud patterns. However, with the application of machine learning algorithms, financial institutions can now analyze large amounts of data in real-time to identify and track new patterns associated with fraudulent transactions, making the detection process more accurate and efficient.Machine learning models can be trained on historical transaction data to recognize patterns that may be indicative of fraud, such as high-value transactions taking place at unusual times, or transactions taking place in locations that are not typical for the account holder. The algorithms can then flag any suspicious activity for further investigation, saving time and resources for the financial institution.Overall, the application of machine learning in fraud detection is helping to enhance security and protect consumers from financial crimes. . Response in Sesotho: Ho ithuta ka mochini ke sebaka se ntseng se tsoela pele ka potlako se nang le litsebetso tse ngata lefatšeng la nete. Mohlala o mong o hlahelletseng ke oa kamoo ho ithuta ka mochini ho sebelisoang ho matlafatsa tsebetso ea bomenemene indastering ea lichelete. Nakong e fetileng, ho lemoha bomenemene haholo-holo e ne e le sehlopha sa litsamaiso tse thehiloeng melaong, tse neng li haelloa ke bokhoni ba tsona ba ho ikamahanya le mekhoa e mecha, e hlahang ea bomenemene. Leha ho le joalo, ka tšebeliso ea li-algorithms tsa ho ithuta mochine, mekhatlo ea lichelete e ka khona ho hlahloba lintlha tse ngata ka nako ea sebele ho tseba le ho latela mekhoa e mecha e amanang le litšebelisano tsa bolotsana, ho etsa hore mokhoa oa ho lemoha o nepahale le ho sebetsa hantle. Mefuta ea ho ithuta ka mochini e ka koetlisoa holima data ea khale ea transaction ho lemoha mekhoa eo e ka bang matšoao a bomenemene, joalo ka litheko tsa theko e phahameng tse etsahalang ka linako tse sa tloaelehang, kapa lipalangoang tse etsahalang libakeng tse sa tloaelehang bakeng sa monga akhaonte. Li-algorithms li ka tšoaea ketsahalo efe kapa efe e belaetsang bakeng sa lipatlisiso tse ling, ho boloka nako le lisebelisoa bakeng sa setsi sa lichelete. Ka kakaretso, tsebeliso ea ho ithuta ka mochini ho lemoha bomenemene e thusa ho matlafatsa tsireletso le ho sireletsa bareki ho litlolo tsa molao tsa lichelete. 