Ngezinyanga ezidlulile, i-digital financial transactions yakhelwe ngokuvamile futhi ngokushesha kakhulu. Nakuba lokhu kuyinto ukuthuthukiswa okwenziwe kakhulu, isakhiwo se-threats esifundeni zihlanganisa kakhulu. Ukusuka ku-identity steal and payment fraud to synthetic financial crimes and data breaches, ukuphucula ukhuseleko se-transaction kuyinto isikhokelo esikhulu emhlabeni wonke. Ngokuqhathanisa, lezi zimpumelelo ezihlangene zihlanganisa ngokuvamile ngenkathi usebenzisa izinhlelo zokusekelwe nezinsizakalo.
U-Harish Kumar Sriram, umbhali owaziwa kwebhizinisi wokubhalisa, ukuguqulwa kwe-credit risk, ukwelashwa kwe-identity-theft, kanye ne-marketing automation, wabhalisela isakhiwo se-AI esithathwe ukuvikelwa kwezohwebo ngokusebenzisa isitifiketi yayo yokuhlola "I-Generative AI-Driven Automation ku-Integrated Payment Solutions: Transforming Transactions Financial with Neural Network-enabled Insights." Ukusetshenziswa kwebhizinisi asebhizinisi, i-Generative AI, kanye ne-smart pseudo-labeling, isifundo se-automation ekukhuthaza ukuhlukaniswa kwe-fraud, ukhuseleko kwamakhasimende ngamakhasimende e-payment, kanye nok
Transaction Security in the Digital Age
I-transactions ye-financial ibhizinisi ngokushesha okuhambelana ne-scale ye-economy yesikhathi se-digital, ebandayo ku-e-commerce platforms, ama-banks ezivamile, izicelo ze-mobile, kanye ne-fintech startups. Izinzuzo ezinzima zokuphepha zithunyelwe yi-proliferation ye-digital payment interfaces. Lezi zenzakalo zihlanganisa i-transit ye-financial data sensitive. Ukusetshenziswa kwezimpendulo ezincinane kungase kuholele ku-data breaches, ukulondoloza kwezimali amakhulu, nokunciphisa ukuxhaswa kwamakhasimende.
Ngo-research yakhe, uSriram isho ukuthi isikhathi esithathwe izinhlelo ezivamile kanye nezinhlelo ezisekelwe ku-static algorithms kanye nokumelana
Ngokuvimbela amamodeli we-AI abakwazi ukuyifaka ama-compliance indicators kanye ne-risk ngokushesha, izakhiwo zingakwazi ukuvikela ukuhlangabezana kwebhizinisi zokusebenza ezingu-complex kanye nokuthuthukiswa kwe-regulatory kanye nokunciphisa isibambiso se-manual kanye ne-audit.
Importance of Generative AI and Neural Networks
I-combination ephakeme ye-neural network architectures kanye
Umklamo we-pseudo-labeling ye-intelligent iyinhlangano olukhulu eyakhiwe ngu-Sriram embhedeni yakhe. Ukusebenzisa idatha eqala e-unlabeled noma e-semi-labeled, kungenziwa ukuqeqesha amamodeli e-AI ebonakalayo. Ngaphezu kwama-transaction amasethi amangqamuzana amangqamuzana amangqamuzana, amamodeli angakwazi ukwandisa ukucindezeleka kwabo ngokuvamile ngokuvumela ama-etiquettes amabili ku-data-points angaziwe futhi ukucindezeleka ngokusebenzisa ukufundiswa kwe-iterative. Lolu hlobo lwezinto ingasiza kakhulu ekubunjweni kwezinto ezingenalutho ezinokuthi zihlanganisa ingozi kodwa akufanele nezakhiwo ezaziwayo
I-Sriram isetshenziselwa i-deep neural networks eyenza ukujabulela i-multidimensional relationships phakathi kwama-data points, ezisetshenziselwa ngemva kokwenza ama-alerts noma ama-approvals e-real-time. Ukuze i-simulate i-high-risk scenarios kanye nokuhlola ukumelana kwekhwalithi ye-synthetic fraud, i-Sriram isetshenziselwa i-generative adversarial networks (GANs). Lezi zibonelelo zihlanganisa ukuqinisa amandla we-AI yokusebenza emkhakheni yomhlaba emangalisayo.
Real-Time Fraud Detection
Ukuhlolwa kwama-fraud yindlela ezivamile zihlanganisa izinjini ezisekelwe nezinsizakalo, izibuyekezo ezimbonini, kanye ne-blacklists. Nakuba lezi zindlela zokusebenza ngokuqondile, zihlanganisa ngokushesha, zihlanganisa, futhi awukwazi ukuxhumana nezinkinga zokusebenza kwezimali ezintsha. Ukuhlolwa kwe-Sriram ibonise imodeli okuzenzakalelayo, emangalisayo kanye nesikhokelo esebenza nge-machine learning kanye ne-real-time data analytics.
I-Fraud Detection Framework ye-Sriram isetshenziselwa uhlelo lwe-hybrid elihlanganisa i-neural networks, i-real-time anomaly detection, kanye ne-fuzzy logic. Ngokuvakashela i-transaction streams ngokuqhubekayo, le nkqubo ibonise ama-patterns ezaziwayo ze-fraud kanye nama-anomali ezivela ezivela ezivamile ezihlangene nezinsizakalo ezivamile.
Enye yezinhlobonhlobo ebalulekile yinkqubo yayo kuyinto umthamo yayo yokuhlola. Ngaphandle kokwenza ukulawulwa okuzenzakalelayo kwezohwebo, it uchofoza ama-cluster zokusebenza phakathi kwezigaba zokuhlanza, ama-time zones, ama-devices, kanye nama-trends ezivamile. Lokhu kuvumela inkqubo ukuhlangabezana phakathi kwe-fraud efanele ne-activity ebonakalayo kodwa ebonakalayo.
Umbhali uqhagamshelane kanjani amamodeli angasetshenziselwa ama-vector of attack nge-simulation of fraudulent transactions using GANs. Ngokufundisa ukujabulela izimo zokusebenza ezingenalutho, njenge-location jumping, i-transaction splitting, kanye ne-identity masking, amamodeli ziye kubaluleke kakhulu ekhuselweni ama-institutions kanye nama-user ngamunye.
Final Thoughts
Ukuhlolwa kwe-Harish Kumar Sriram inikeza ukubukeka kwe-futures ye-intelligent and secure financial transactions eyenziwe nge-generative AI. Nge-focus enhle ku-real-time fraud prevention, ne-neural-network-enabled automation, kanye ne-ethical AI practices, le nophuhliso inokukwazi ukubeka umugqa olutsha yokuthuthukiswa kwe-payment technology.
“I-Generative AI inikeza izinzuzo zokuvimbela, ukuhlaziywa kanye nokuvimbela izinhlelo zokuhweba ngesilinganiso, ngokuvumelana nokuvimbela ukhuseleko kanye nokuvumelana. I-Generative AI inikeza izinzuzo zokuhweba ezisebenzayo, ezikhuthazayo ukuhlangabezana nokuvimbela, futhi zokusebenza ngokushesha ku-compliance yokusebenza kwezimali.”
