Big Data Analytics in Banking
Ahmed Musa
Recorded Books Β· ΠΠ·Π²ΡΡΠ΅Π½ΠΎ ΠΠ Archie (ΠΎΡ Google)
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7Β Ρ. 2Β ΠΌΠΈΠ½.
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ΠΠ·Π²ΡΡΠ΅Π½ΠΎ ΠΠ
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ΠΠΎΠ±Π°Π²ΠΈΡΡ ΠΎΡΡΡΠ²ΠΎΠΊ Π΄Π»ΠΈΠ½ΠΎΠΉ 29Β ΠΌΠΈΠ½.? ΠΠ³ΠΎ ΠΌΠΎΠΆΠ½ΠΎ ΡΠ»ΡΡΠ°ΡΡ Π² Π»ΡΠ±ΠΎΠ΅ Π²ΡΠ΅ΠΌΡ, Π΄Π°ΠΆΠ΅ ΠΎΡΠ»Π°ΠΉΠ½.Β
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ΠΠ± Π°ΡΠ΄ΠΈΠΎΠΊΠ½ΠΈΠ³Π΅
Big data analytics in banking refers to the use of vast amounts of structured and unstructured data to enhance decision-making, improve customer service, and optimize operations. With the rise of digital banking, financial institutions now have access to an unprecedented amount of data from customer transactions, online behavior, social media, and market trends. By analyzing this data, banks can gain valuable insights into customer preferences, risk profiles, and emerging market opportunities. This allows them to offer personalized financial products, predict customer needs, and identify potential fraud or credit risks more effectively.
ΠΡΠ΅Π½ΠΈΡΠ΅ Π°ΡΠ΄ΠΈΠΎΠΊΠ½ΠΈΠ³Ρ.
ΠΠΎΠ΄Π΅Π»ΠΈΡΠ΅ΡΡ Ρ Π½Π°ΠΌΠΈ ΡΠ²ΠΎΠΈΠΌ ΠΌΠ½Π΅Π½ΠΈΠ΅ΠΌ.
ΠΡΠΎΡΠ»ΡΡΠΈΠ²Π°Π½ΠΈΠ΅ Π°ΡΠ΄ΠΈΠΎΠΊΠ½ΠΈΠ³
Π‘ΠΌΠ°ΡΡΡΠΎΠ½Ρ ΠΈ ΠΏΠ»Π°Π½ΡΠ΅ΡΡ
Π£ΡΡΠ°Π½ΠΎΠ²ΠΈΡΠ΅ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ Google Play ΠΠ½ΠΈΠ³ΠΈ Π΄Π»Ρ Android ΠΈΠ»ΠΈ iPad/iPhone. ΠΠ½ΠΎ ΡΠΈΠ½Ρ
ΡΠΎΠ½ΠΈΠ·ΠΈΡΡΠ΅ΡΡΡ Ρ Π²Π°ΡΠΈΠΌ Π°ΠΊΠΊΠ°ΡΠ½ΡΠΎΠΌ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈ, ΠΈ Π²Ρ ΡΠΌΠΎΠΆΠ΅ΡΠ΅ ΡΠΈΡΠ°ΡΡ Π»ΡΠ±ΠΈΠΌΡΠ΅ ΠΊΠ½ΠΈΠ³ΠΈ ΠΎΠ½Π»Π°ΠΉΠ½ ΠΈ ΠΎΡΠ»Π°ΠΉΠ½ Π³Π΄Π΅ ΡΠ³ΠΎΠ΄Π½ΠΎ.
ΠΠΎΡΡΠ±ΡΠΊΠΈ ΠΈ Π½Π°ΡΡΠΎΠ»ΡΠ½ΡΠ΅ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΡ
ΠΠ½ΠΈΠ³ΠΈ, ΠΊΡΠΏΠ»Π΅Π½Π½ΡΠ΅ Π² Google Play, ΠΌΠΎΠΆΠ½ΠΎ ΡΠ°ΠΊΠΆΠ΅ ΡΠΈΡΠ°ΡΡ Π² Π±ΡΠ°ΡΠ·Π΅ΡΠ΅.
