The Future of Artificial Intelligence in Banking provides how Artificial Intelligence (AI) transform baking in future. What impact will AI have on banks, Which AI applications are relevant for banking and more important question s are answered in report.
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The report “Artificial Intelligence in Banking 2017” research report examines the most significant uses of AI in retail banking, in both front-office and back-office implementations. Artificial Intelligence (AI) has reached the stage where it is sufficiently advanced and affordable to warrant practical implementation in financial services.
Banks are busy exploring ways in which they can harness the power of AI to streamline internal processes and improve the customer experience.
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Artificial Intelligence in banking report 2017 will explore what AI applications are relevant in banking at this time, examine where Artificial Intelligence (AI) is already making an impact, and offer recommendations on how banks should proceed. Several aspects of banking are ripe for AI-driven intervention.
Many of their data-driven back-office processes are high volume and repetitive in nature, and hence are ideal candidates for intelligent automation. At the front end, with the majority of customer interactions occurring through digital channels, there is also considerable scope for AI to improve the quality of and add value to the user experience.
Companies mentioned in this report: Admiral, Amazon, Atom Bank, Bank of America, DataVisor, Ernest, EyeVerify, Facebook, Google, IDnow, Kasisto, Leddo, Moneyhub Enterprise, Olivia, PayPal, Personetics, Plum, POSB, Starling Bank, USAA, TrustingSocial, Wells Fargo, ZestFinance.
Key findings include in this report -
Artificial Intelligence (AI) encompasses a wide range of technologies, including robotic process automation (RPA), natural language processing (NLP), advanced data analytics, and image analytics. Use of these technologies will help banks improve both front-office and back-office processes.
Customer-facing uses of Artificial Intelligence (AI) include chatbots that improve communication between banks and their customers, advanced analytics that can offer proactive advice to consumers and take simple financial decisions on their behalf, and facial recognition that improves onboarding and makes it easier for consumers to log into their accounts. Back-office AI implementations include algorithms that can identify and block cases of fraud and money laundering, and analysis of non-traditional data to assess the creditworthiness of borrowers who lack standard credit records.
Additionally this report insight into following -
- The particular manifestations of AI that have the most relevance for banking.
- How leading banks are already implanting AI-based solutions.
- The factors banks need to address when introducing AI applications.
Report at: www.reportsnreports.com/.aspx?name=1117407 .
Table of Contents:
- EXECUTIVE SUMMARY 3
1.1. Market summary
1.2. Key findings
1.3. Critical success factors
- AI WILL TRANSFORM RETAIL BANKING
2.1. What is AI?
2.2. What impact will AI have on banks?
2.3. Which AI applications are relevant for banking?
2.3.3. Advanced data analytics
2.3.4. Image analytics
2.3.5. ML and deep learning
- AI WILL IMPACT BANKING IN SEVERAL WAYS
3.1. Customer-facing implementations
3.1.1. Chatbots and virtual assistants
3.1.3. Identity verification using biometric data or document scanning
3.2. Back-office implementations
3.2.1. Anti-money laundering and fraud detection
3.2.2. Underwriting and credit assessment
- RECOMMENDATIONS FOR IMPLEMENTING AI
4.1. Improve data quality
4.2. Partner with fintech specialists
4.3. Plan for potential execution risks
4.3.1. Malfunctions and lack of efficacy
4.3.2. Algorithm bias
4.3.3. Lack of transparency
4.3.4. Data privacy issues
5.1. Abbreviations and acronyms
5.3. Further reading
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