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The Future of Artificial Intelligence in Banking

Published By :

GlobalData

Published Date : Jun 2017

Category :

Banking

No. of Pages : 35 Pages

The Future of Artificial Intelligence in Banking

Summary

"The Future of Artificial Intelligence in Banking", 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. This report will explore what AI applications are relevant in banking at this time, examine where AI is already making an impact, and offer recommendations on how banks should proceed.

The report offers insight into -
- 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.

Scope

- AI encompasses a wide range of technologies, including robotic process automation, natural language processing, 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 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.

Reasons to buy

- Discover where AI will have the most impact upon the delivery of banking services.
- Learn how your competitors are already using AI to improve customer outcomes and profitability.
- Understand what issues you must resolve in order to successfully launch AI-based services.
Table of Contents
1. EXECUTIVE SUMMARY 3
1.1. Market summary 3
1.2. Key findings 3
1.3. Critical success factors 3
2. AI WILL TRANSFORM RETAIL BANKING 7
2.1. What is AI? 7
2.2. What impact will AI have on banks? 7
2.3. Which AI applications are relevant for banking? 8
2.3.1. RPA 8
2.3.2. NLP 9
2.3.3. Advanced data analytics 10
2.3.4. Image analytics 11
2.3.5. ML and deep learning 12
3. AI WILL IMPACT BANKING IN SEVERAL WAYS 13
3.1. Customer-facing implementations 13
3.1.1. Chatbots and virtual assistants 13
3.1.2. PFM 17
3.1.3. Identity verification using biometric data or document scanning 20
3.2. Back-office implementations 24
3.2.1. Anti-money laundering and fraud detection 24
3.2.2. Underwriting and credit assessment 26
4. RECOMMENDATIONS FOR IMPLEMENTING AI 29
4.1. Improve data quality 29
4.2. Partner with fintech specialists 29
4.3. Plan for potential execution risks 30
4.3.1. Malfunctions and lack of efficacy 30
4.3.2. Algorithm bias 30
4.3.3. Lack of transparency 30
4.3.4. Data privacy issues 31
5. APPENDIX 32
5.1. Abbreviations and acronyms 32
5.2. Bibliography 32
5.3. Further reading 34



List of Figures
Figure 1: AI will enable increasingly sophisticated analysis of data for the benefit of customers 11
Figure 2: Erica provides proactive financial insight and advice to Bank of America customers 14
Figure 3: Personetics Anywhere uses a conversational interface to convey information to customers 15
Figure 4: Plum, Ernest, and Olivia aim to encourage better financial behavior through intelligent alerts and prompts 19
Figure 5: Wells Fargo is one of several dozen banks that offers Eyeprint login 21
Figure 6: USAA was one of the first US providers to offer facial recognition 22
Figure 7: Video-Ident from IDnow reads the holographic information in German ID cards to confirm authenticity 23
Figure 8: TrustingSocial uses social media data to verify consumers identities and check their creditworthiness 27

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