
은행
Banks Can Prosper With Enterprise AI
Banks are facing challenges from all sides, including emerging threats from new technology-enabled Fintech competitors, stricter regulatory requirements, and pressure to simplify the client experience while simultaneously reducing costs. Implementing AI and machine learning in banking capitalizes on a once-in-a-generation opportunity for your bank to expand market share, deepen customer relationships, and compete for and win new business — all while efficiently complying with regulations and fighting financial crime.
See how banks are winning with AI.
Banks are facing challenges from all sides, including emerging threats from new technology-enabled Fintech competitors, stricter regulatory requirements, and pressure to simplify the client experience while simultaneously reducing costs. Implementing AI and machine learning in banking capitalizes on a once-in-a-generation opportunity for your bank to expand market share, deepen customer relationships, and compete for and win new business — all while efficiently complying with regulations and fighting financial crime.
See how banks are winning with AI.
AI와 은행
Both companies and consumers expect their banks to understand who they are, anticipate their needs, and be ready with relevant financial solutions. Banks need to deliver these solutions seamlessly across multiple channels, offering convenient access from any location, on any device. To stay competitive, you must nurture existing relationships while finding new clients in new markets. You must also compete aggressively to find the best businesses, rather than waiting for businesses to find you. AI and the application of machine learning in banking has the power to address these goals by leveraging data from your existing clients — including how their financial needs have evolved and their channel preferences.
고객 경험
- Determine which client is likely to need specific products or services
- Deepen your relationships with customers
- 고객의 니즈를 예측하고 새로운 니즈가 발생할 때 식별
- 적합한 목표 제안
- 고객에게 지원이 필요할 때 지원 확보
- 분석을 활용하여 고객의 가격 민감도 및 선호도 이해
대출
- 더욱 정확한 신용 모델 구축
- 리스크 조정 수익이 가장 좋은 비즈니스 발굴 및 경쟁
- Actively manage your client portfolio
- 뛰어난 분석 기능으로 소규모 비즈니스 신용 분야 선도
- Proactively intervene when clients experience financial stress
- 더욱 정확한 손실 예측
금융 산업
- Reduce middle and back office costs related to process failures and error corrections
- 가격 책정을 개선하고 최고의 비즈니스 기회 포착
- 거래 체결 및 절차 최적화
- Match investment opportunities to potential investors
- 적합한 고객에게 연구 보고서 제공

은행 산업의 고부가가치 분석 과제
There are hundreds of enterprise AI applications in every function and business line in a bank. By using AI in consumer, investment, and commercial banking, your bank — whether large or small — can drive revenue growth, differentiate your brand by offering a superior client experience, reduce operational costs while improving quality, and improve risk management effectiveness and efficiency.
은행 직무별 DataRobot 혜택:

AI가 어떻게 은행 실적을 높이는지 자세히 알아보십시오.