The Future of Artificial Intelligence in Banking

  • ID: 4327719
  • Report
  • 35 pages
  • GlobalData
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FEATURED COMPANIES

  • Admiral
  • Bank of America
  • EyeVerify
  • IDnow
  • Moneyhub Enterprise
  • Personetics
  • MORE
The Future of Artificial Intelligence in Banking

Summary

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.

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.

Key findings include in this report:
  • 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 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.
Critical success factors
  • Improve data quality: AI algorithms depend on access to high quality data to work effectively. Banks must move away from siloed and fragmented databases towards a single view of their customers. This will give the algorithms access to enough data to make effective decisions.
  • Collaborate with AI specialists: AI is an incredibly complex field, and banks are best advised to partner with AI specialists. This will enable them to launch AI-based services quicker and cheaper than if they were to develop solutions in-house.
  • Address potential execution risks:Banks need to be aware of the possible pitfalls of using AI, such as algorithm bias, a lack of transparency around decision-making, and concerns with data privacy. Steps should be taken to minimize these risks.
The report "The Future of Artificial Intelligence in Banking" examines the most significant uses of AI in retail banking, in both front-office and back-office implementations.

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.
Companies mentioned in this report: Admiral, Amazon, Atom Bank, Bank of America, DataVisor, Ernest, EyeVerify, Facebook, Google, IDnow, Kasisto, Lenddo, Moneyhub Enterprise, Olivia, PayPal, Personetics, Plum, POSB, Starling Bank, USAA, TrustingSocial, Wells Fargo, ZestFinance.

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.
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Note: Product cover images may vary from those shown
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FEATURED COMPANIES

  • Admiral
  • Bank of America
  • EyeVerify
  • IDnow
  • Moneyhub Enterprise
  • Personetics
  • MORE
1. EXECUTIVE SUMMARY

1.1. Market summary
1.2. Key findings
1.3. Critical success factors
2. 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?
2.3.1. RPA
2.3.2. NLP
2.3.3. Advanced data analytics
2.3.4. Image analytics
2.3.5. ML and deep learning
3. AI WILL IMPACT BANKING IN SEVERAL WAYS
3.1. Customer-facing implementations
3.1.1. Chatbots and virtual assistants
3.1.2. PFM
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
4. 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. APPENDIX
5.1. Abbreviations and acronyms
5.2. Bibliography
5.3. Further reading

List of Figures
Figure 1: AI will enable increasingly sophisticated analysis of data for the benefit of customers
Figure 2: Erica provides proactive financial insight and advice to Bank of America customers
Figure 3: Personetics Anywhere uses a conversational interface to convey information to customers
Figure 4: Plum, Ernest, and Olivia aim to encourage better financial behavior through intelligent alerts and prompts
Figure 5: Wells Fargo is one of several dozen banks that offers Eyeprint login
Figure 6: USAA was one of the first US providers to offer facial recognition
Figure 7: Video-Ident from IDnow reads the holographic information in German ID cards to confirm authenticity
Figure 8: TrustingSocial uses social media data to verify consumers’ identities and check their creditworthiness
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  • Admiral
  • Amazon
  • Atom Bank
  • Bank of America
  • DataVisor
  • Ernest
  • EyeVerify
  • Facebook
  • Google
  • IDnow
  • Kasisto
  • Lenddo
  • Moneyhub Enterprise
  • Olivia
  • PayPal
  • Personetics
  • Plum
  • POSB
  • Starling Bank
  • USAA
  • TrustingSocia
Note: Product cover images may vary from those shown
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