NextGen Banking London - live blog

NextGen Banking London - live blog

Welcome to Finextra's live coverage of NextGen Banking London. The event promises to explore what the AI revolution means for banks - and what financial institutions need to do to benefit from AI, and ensure their customers do so as well.

16.17: That concludes NextGen Banking London 2018. Thank you very much for joining us on Finextra for a flavour of the event.

16.16: Machine learning offers us an ability to look into the future, says Akerkar. This will allow banks to change the nature of their business. On a customer perspective, it offers a world without spam, which would be most welcomed. Finally, it can allow the banks to stay ahead of 'bad actors' and target cyber crime. Together, he says that these three points will make a fundamental change to financial services.

16.11: What do you want to look like to your customer? Dubey points to the importance of the end-to-end customer journey, and the importance of a consistent approach in this regard. He says that KYC is a bigger question in the minds of banks than AI today. It is not good enough to solve one issue in the customer journey, the vision has to be across the whole journey, which is where banks are heading in the future.

16.07: Thinking about 2030, a question from the audience asks what banks should be wary of on the technology journey from here to there? Husain says that if you think about architecture, it is heading towards microservices. This is what you need to be implementing if you are building for the future.

16.01: Dubey notes that organisations have thrived on siloes, creating internal competition. But today's developments are so interconnected, you need to eliminate the siloes to succeed. He adds that you cannot forget the human aspect of it - if they are not shown the integrated nature of the journey, it will not be successful.

15.57: Husain says we need to think whether the data, algorithm, and implementation as used by people are biased. All three of these aspects need to be looked at in detail before going in to production.

15.56: There is a fear factor around AI, Wright notes. As we raise and train AI systems, how do banks look at their social responsibilities around transparency? Hunter agrees that transparency is important. Any kind of data driven approach is being used mainly in areas where human analysis would not work as well. Additionally, financial exclusion is a big problem for the world, and we need to think about how technology impacts on that. If we train models that are (unintentionally) based on a bias, we have a problem.

15.52: If you want to go fast, go alone, but if you want to go long, go together, says Akerkar. This means bringing your customers along on the AI journey, reassuring them that whatever you are doing with AI is being done for their benefit. You also need to bring along your workforce in this way. Then the board and shareholders need to be reassured that all innovations are subject to risk controls. He adds that banks need to help the regulators come with them on the journey as well. Finally, collaboration is key, across business units, picking up on the silo theme that has come up today.

15.47: Cackett draws on the example of linear organisations versus exponential organisations that we heard earlier. Both will do many of the same tasks, but the exponential team has a strong focus on the third horizon. Agile organisations will also create something similar to an information factory with three main goals - discovery (data), operationalise (to monetise), and monitor new solutions.

15.42: Dubey says that AI has a collection of tools that will help financial services enhance their customer offerings, and that we are at the start of an exciting journey. Hunter explains how his organisation works with banks to try and enhance their SME lending businesses, using AI in this regard in a very targeted way.

15.38: Cackett notes that organisations are having to transform at an increasing rate just to stand still. He adds that other organisations and verticals have been better at saying what data means and applying it, and that financial services hasn't necessarily been very clever in asking that question and applying it to customers.

15.35: Time now for our final panel discussion of the day, which promises to provide a checklist for AI success on the road to 2030. The panel is comprised of Abhijit Akerkar, head of Applied Sciences, Business Integration at Lloyds Banking Group, 
Doug Cackett, EMEA Big Data and IoT practice lead with Dell EMC, 
Sameer Dubey, director, head of Payments for Barclays, 
Sean Hunter, CIO at ACORN machine, and 
Syed Husain, enterprise architecture manager with Accenture. Our moderator is Gary Wright, content director at Finextra.

15.28: A fascinating psychological insight there, clearly it is important not to forget the human element in the technology discussion. A mindset shift that has an open mind, open heart and open will is essential.

15.22: Fear can be a short-term motivator, but an engaged and happy environment is more conducive to long-term success, Cooper notes. Additionally, transparency breeds trust, which is something that holds true across financial services.

15.18: Cooper says it is we as human beings that are exponential. She brings up several quotes from philosophers that have the common message - know yourself. Machines by their very nature are psychopaths, we tell them what to do and they do it. Algorithms are opinions embedded in code. Cooper notes we need to prioritise human awareness as a sense of urgency.

15.15: We need a big mindset shift in how we think of ourselves as humans, and how we thing about technology, so that we can avoid a world of technofear in the future, Cooper says. We need to be careful to avoid unintended consequences from terminology - citing citizen and consumer as essentially the same thing but one has a slightly different connotation to the other.

15.10: The exponential change of the fourth industrial revolution is again mentioned today, with Cooper noting that while our living standards have improved, there has been population growth, climate change and rising sea levels as part and parcel of this change. She says that our individual and collective changes have never been more important than they are today.

15.05: Time for another keynote presentation now, this time looking at the human element of the AI revolution. Tabitha Cooper, chief communicator at Nordea is giving this presentation, and she kicks things off with a short video. The video focuses on a member of the private banking support staff at Nordea, discussing the possible impact of AI and chatbots on his role in the bank. He sees a key role for his job to help educate the AI that the bank is bringing online in his area. Cooper says that this shows a lot of bravery on his part to lean in to this role.

15.02: Looking to the future, Scott says that we will be able to understand our data much better, which will lead to far greater products and service quality. We will be able to bring in a broader set of tools that put data at the centre, agrees Reid. This will have touchpoints into modern and legacy solutions. Balk-Møller says that robotics will not replace everyone, there is an advisory responsibility that banks have. Here, the technology can assist the banking staff, rather than replacing them.

15.00: There is going to be a pipeline of new regulatory initiatives that robotics will be critical to help manage, says Balk-Møller. He notes that this means the regulators could then become quicker.

14.47: There is a lot of great technology out there already, the most important aspect is the top down vision, says Scott. He notes that the Commerzbank CEO has stated that the bank will be 80% digital by 2020. You need the balance of the young technologist coming through and the experienced technologist in the bank, but most importantly you need the culture in the institution to allow success.

14.55: How long does an RPA journey take? Balk-Møller says that, three years in to the project at Danske Bank, they are still at the beginning of the journey. There are still things they don't know yet - what will be invented next - and this can take longer than some people expect. You want to play with the new technologies, sandboxing it, to see what is possible and perhaps surprise yourself along the way.

14.50: Balk-Møller says that end-to-end processes are obviously important, but you need to have the vision of how this works before you begin the RPA, otherwise you run the risk of implementing this in a somewhat siloed environment. It is possible to get blinded by the tools, and he gives the examples of business units asking for robotics as it sounds cool, rather than as part of an overall company strategy. Scott agrees that some banks think in a siloed fashion, which ends up in just papering over the cracks.

14.46: Reid says he advises customers to put data at the centre of their processes. This is absolutely the case with RPA. He says that anyone with an automation agenda needs to think about the nature of the transactions end-to-end, not just at the end process. RPA is about giving people more meaningful work to do, Scott says. Data is the core for success in anything.

14.44: Scott says that starting small is definitely the way to go with robotics. In a lot of the western European markets you are getting an aging population in the back office operation processing staff is over 50. He says the reason for this is that people are not interested in mundane operations - this is where robotics can come in and take the load, creating an opportunity for more exciting jobs to be created for the human staff. It is all about the data, he adds. Robotics is no good without the right data.

14.41: There are areas where robotics has greater applicability than others, Reid says, and there is a maturity curve for organisations to go through here. Looking for the sweet spot for this technology is a sensible approach. He adds that robotics alone generally won't solve a problem in most cases - having a robot replicating a process that was designed for a human does not make that much sense.

14.36: Reid says one of the challenges this space has had is what robotics means in the back office of a financial institution. He says the expectation of robotics has possibly run ahead of the capabilities. While we might be thinking of self-driving cars or IoT, this is not necessarily what is meant by robotics in banking. The end-to-end solution doesn't just lie in one product, he adds. Which areas of the bank does RPA fit in to? Balk-Møller says it particularly bubbles up in the back office shared services.

14.34: Following that case study, it is now time for a panel discussion on whether robots can make the STP vision a reality. Our moderator for the discussion is David Bannister, principal analyst at Ovum. Balk-Møller is staying to be part of this panel, and is joined by Dan Reid, CTO at Xceptor, and 
Rob Scott, managing director, head of Custody, Collateral & Clearing with Commerzbank AG

14.31: Dankse has focussed a lot on training and standardisation, as well as supporting the setup of RPA. In closing, Balk-Møller says that organisations on this journey should start small and be realistic. As they scale up, they go into areas of the business. It is also vital to communicate success and create awareness - highlighting how robotics can eliminate the human role in basic copy and paste work can actually be welcomed by the workforce. Also, sponsorship from the senior management is vital for a programme such as this.

14.28: In two and a half years, Danske Bank has built its CoE, implemented the 24/7 monitoring team, implemented the RDA pilot and best practice, seen the first BPM and RPA project go live, and initiated the machine learning and robotics pilot, among other innovations. How does this fit in with strategy? Ideally, the technology should be pushing the business strategy. Balk-Møller says that Danske isn't there yet, but it is getting to a point where it is supporting business strategy.

14.24: The Danske Bank CoE focussed on four main areas: best practice, best team, reliable platform and knowledge hub. The stability of a robot is as stable as the systems that it works across, if one system goes down, the robot goes down. So the bank worked on streamlining the systems that the robots worked on.

14.20: Robotics can bring speed, reduce costs and add efficiency to banking processes. But you need to select the right approach. Danske went for a federated model, which allows business units to control the priority of development. It also means that there is a centre of excellence (CoE).

14.16: Danske Bank's starting position is as a 100+ year old banking with product complexity and legacy systems, so it wanted to address the challenge of meeting today's raised customer expectations. Bank processes are digital, but paper signatures were required - just one example of the many challenges that needed addressing.

14.08: Coming up in a couple of minutes, the afternoon session will be getting underway with a case study on robotic process automation (RPA) and efficiency. This will be presented by Anders Emil Balk-Møller, program manager, Robotics Centre of Excellence, Process Automation at Danske Bank.

12.57: That concludes the panel discussion and it is now time for the lunch break. Join us later this afternoon, at 2.10pm, for more insights and analysis of AI applications for financial services.

12.55: When will there be wholesale change to the customer experience? Blanc sees currently there are small changes, such as chatbots, and the steady curve will continue into the future. Drummer agrees - this isn't a big bang, but rather a case-by-case update spreading throughout the customer experience. Hussain also sees that there will be a steady growth over the next two to three years. He also says that not every problem is an AI problem - don't just throw AI at everything, use it intelligently. Using computational power across the organisation will contribute to an enhanced customer experience, concludes Schooley.

12.47: How do you make the business case for AI to management? Every organisation has its own internal politics to some degree. Blanc explains how he went to the top to see the two key business issues that senior management wanted addressed, and then identified how AI could be applied to these issues. As it came from the top, it took priority for the rest of the business and negated any possible political issues that middle management may have had otherwise.

12.44: A question from the audience asks that interactions with customers involves some level of emotional intelligence - can AI ever take this over on its own? Schooley says it is hard from a regulatory perspective - how do you prove that you as a business are actually listening to the customer? Blanc says that machine learning can be used to see what kind of voice is required in a specific circumstance to a specific customer, and that this is a really exciting possibility. Hussain says that the answer to the audience question is yes in the long-term, but we won't get there in the short- to medium-term.

12.40: Do you need to give customers an exit opportunity from AI interaction, Schooley asks. People want the civil right not to be profiled in some ways. It is up to the banks and vendors to highlight the benefits of these interactions, but also have a possibility to opt out if the customer wants to.

12.37: So many areas of a bank want to analyse customer data, it is critical they have policies in place in terms of how they interact with that data, says Blanc. He adds that, aside from data, the business managers can be a challenge in terms of who wants to implement AI to improve the customer experience, and those that don't. There's an educational piece here to address.

12.34: One challenge today is to stay compliant and still deliver consistent customer experience, Hussain says. Drummer adds that what banks are doing and not doing is restricted to some extent by the compliance and regulation perspective that they face in hundreds of domains around the world.

12.30: Schooley says there are two good use cases that his company is looking at, one of which is around fraud algorithms. Refunding instantly is a better customer outcome than calling the customer during their day. The relationship with the customer is highly predictive of how your customer views you. They are also looking at how the customer interacts with the mobile app, to enhance the usability and reduce pain points.

12.26: Facilitating an architecture for an AI-enabled platform is not that different to any other platform, says Hussain. The technology is fairly similar, agrees Drummer, it is about how you apply it.

12.23: Blanc says the ability to compare multiple campaigns to multiple groups is an important area where AI could build a fast feedback loop to quickly identify and deliver customer-centric solutions.

12.21: How do you distinguish the needs between corporate and retail? Schooley notes there are different regulatory frameworks to engage with. He says that the way the regulators catch up with AI will play a big role here.

12.18: Following that presentation, we have a panel discussion looking at AI and the customer journey. Finextra's Milne is back on stage to moderate this discussion, and she is joined by Francois Blanc, head of Customer Experience - Customer and Innovation at Santander UK
, Daniel Drummer, vice president, Corporate & Investment Bank FinTech with J.P. Morgan, 
Naveed Hussain, AI architect and evangelist for Microsoft UK, and 
Paul Schooley, COO at CashPlus.

12.15: Make it memorable from the beginning, because it is not the AI but the customer experience that will help your organisation survive, Pelsmaker says.

12.14: Find the right balance between service and sales, Pelsmaker advises. If the chatbot feels like a marketing campaign, the user will get frustrated and log off. Additionally, you need a consistent experience across touchpoints - one truth across the organisation. This needs a central knowledge base. You also need to look at the end-to-end customer journey to avoid switching.

12.12: ING has built a number of chatbots in different countries. ING Belgium launched Chatbot Marie on Facebook Messenger in August 2017 for the cards and daily banking domain for simple card issues. Pelsmakers says that Marie has deflected a lot of simple customer service requests from ING staff, freeing them up for more value added activities.

12.06: A checklist Pelsmakers highlights includes insights such as understand the top reasons why people call, and increase the findability of the digital answer/action. Also, try to understand the customer intent - are you booking a ship or shipping a book? Chatbots also need to react in a human-like manner, with real empathy and humour.

12.01: Pelsmakers is focussing on the digital assistant element of AI. Three in four consumers prefer self-service, and even more strikingly one-third of people would prefer to clean a toilet than call a help centre! 30% of callers have tried to find the solution online before calling, which highlights how frustrating they must already be before calling.

11.58: Time now for a case study presentation from Stephanie Pelsmakers, chapter lead digital customer experience at ING Bank, who is addressing how to redefine the customer experience through AI.

11.50: Five years from now, where will AI be in banking and regtech? A more common question being asked at board level will be around specific goals a company wants to achieve, and where AI then fits into this. The consumer experience will also push financial institutions to enhance their offerings to keep up with the Googles and Amazons of the world. Now we are in a real-time banking world, AI has a key role to play in this instant world, particularly with regard to fraud. Voice recognition is picked out as being a really interesting area to watch.

11.38: Data is quite freely available, but it can be quite dense and indigestible. This is an industry issue that needs to be addressed.

11.32: Every AI application has to start in a sandbox, but it doesn't stop there. Once you have a business case for it, you move to a prototype stage where you solve its issues. Following success here, you can move it out enterprise-wide.

11.31: You need the domain expertise. Regulation is complicated, you need to find the domain experts that can qualify the output you are receiving from AI.

11.27: Regtech allows you to do more with less. This lowers costs, which allows you to service more customers, such as the unbanked market, for example.

11.25: AI machine learning can bring new ways of operating. AI can pick up manually-intensive tasks, freeing up the humans do more strategic work and deliver value.

11.23: You also need to understand the domain and see where it is being applied. The classic quote 'garbage in, garbage out' is mentioned to highlight this.

11.18: What are the biggest opportunities for AI and regtech? Data and the ability to play is really key. New regulations are bringing in more data than ever before, so having the right focus when sifting through this is key. There is an opportunity to move to being truly digital, but only when the data challenges have been addressed.

11.11: Time for the next panel discussion, which is exploring where AI meets regtech. This session is unique today in that it is under the Chatham House rules.

11.10: AI is something that the financial services sector is grappling with now. Nettleship closes by saying that the future for AI is exciting.

11.07: Reversibility is another important pillar of ensuring the correct outcomes, along with accountability. Someone within the organisation, preferably within the boardroom, should be responsible and accountable for AI implementations, Nettleship says. You also must ensure that security standards apply to AI as they would across the rest of the organisation.

11.05: AI needs to deliver reliable and valid results in order to add value. Data is core to driving this, but governance is critical. You need to ensure you have the correct controls in place to manage this. Nettleship says that focus is also key to ensuring the correct outcomes, deploying AI in specific areas to ensure success. General AI is still illusive, but Nettleship says this may just be 5-10 years away.

11.01: The World Economic Forum tried to map out the complexity of AI, covering all sectors, functions, capabilities, actions and cognition involved. How we govern and manage cognition, and how we can apply it to our businesses, are critical questions according to Nettleship.

10.58: Nettleship opens by pointing out that the definition of 'intelligence' differs depending on where you are in the world. Applying that to AI, simply put it is machines that have the cognitive capabilities to think on our behalf. Cognition and AI is a key area of investment for added business value.

10.55: 'AI - the Validity and Reliability Conundrum' is the topic of the next keynote. This is being presented by Richard Nettleship, associate partner, AI Business Consulting with Atos UK and Ireland.

10.30: Time now for a short break. We will be back with the next keynote at 10.55.

10.29: If you refuse someone a loan, they have the right to know why. You can't just tell them that the deep learning system found an issue, Kinlen says. Banks have a tremendous opportunity, says Blalock, but they need to update technology and processes to take advantage of the AI opportunity.

10.25: 93% of CEOs questioned in an IBS survey said that they were not happy with the outcome of innovation programmes. Durodié says that this is down to a lack of overall vision, hopping from project to project rather than having an overall strategy and engaging with the board.

10.22: Durodié says that the longer a company reimagines their new business model, the greater the number of people they will possibly have to let go. She says companies should try to retain as many people as possible by retraining them as quickly as possible. She says this approach is ethically visionary.

10.19: Kinlen highlights that you really need to explain to customers what you are doing with their data. But the message to the customer is really important in this regard, in explaining what the benefit to them is.

10.16: We don't all operate in isolation, there is regulatory oversight and quite often regulators have a 20th century perspective, Williams notes. Blalock says the regulators don't want to stifle innovation, but at the same time they want to ensure stability. How do you use data in a controlled way? That is a question that is being worked on.

10.13: All banks are involved with digital projects, but not all have made the shift to digital, Blalock says. It is not simply a case of digitising your existing processes, banks need to think digitally, which is completely different to their existing processes.

10.11: Everyone knows this impact that AI technology will have, but there is a possible issue in the middle management of institutions, says Rohatgi, particularly on the retail banking side. People need to understand what AI can do, but this experimentation needs to happen in a safe, sandbox environment. You need the data scientists, which most retail banks lack.

10.09: Blalock says that advanced analytics is an extension of the big data conversation from the past couple of years. Most big banks that he talks to have over 100 projects going on around AI and use cases. The conversation needs to be led at board level, and focus on what is improving the customer experience. He also agrees that the main challenge is how to avoid siloes in this regard.

10.06: Durodié makes the important point that innovation shouldn't be occurring in siloes within the business. The board needs to be up to speed with new technologies such as AI and machine learning in order to make sure the silo issue doesn't happen - the leadership in this area needs to come from the top down. She says she is yet to see a joined up exercise vertically and horizontally. Egos need to stay out of the conversation, she cautions.

10.04: Chatbots have a big role to play in HR, Kinlen notes. This is an internal area where numerous similar queries are going to be coming in, so the technology can offer great innovative assistance here.

10.00: Rohatgi says that he covers questions such as what capabilities his bank has, what solutions exist, and where they need help. He makes the point that there is a lot of data available, but the key area of focus has to be customer-centric so that you can deliver value to the customer. Ultimately what you want to achieve is faster, streamlined and more accurate capabilities, Rohatgi says.

9.56: Durodié says that most of her work today is with people at board level, changing their mindset to make AI an important area of business transformation. Kinlen agrees that he gets questions about what AI can do, noting that transition can be slow. He also flags up the legal issues that can be impacted - AI can go through contracts that may have been put in front of a junior lawyer previously.

9.52: Now it is time for our first panel discussion of the day. The topic under the spotlight is whether the next generation of banking is really AI? Moderator of the panel is Jonathan Williams, principle consultant of Mk2 Consulting. Speakers include Mike Blalock, general manager, Financial Services Industry (FSI) vertical at Intel Corporation
, Clara Durodié, founder and CEO of Cognitive Finance Group, 
Philip Kinlen, data scientist at AIB
, and Roshan Rohatgi, senior digital innovation lead for RBS.

9.49: In terms of lessons learned across AI adopters, Sheppard says that early adopters are digitally mature, adopt AI in core activities, and focus on growth over savings. Their success factors include use cases and sources of values, they pick multiple vendors, and have an open culture and organisation. She says that issues with every customer include data, which is siloed and difficult to get to. Issue number two is that culture trumps everything.

9.45: The presentation covers an early adopter use case, on the medical insurance claims side. The challenge was to reduce fraudulent auto claims. Sheppard explains how Intel Saffron AI unified multi-source data into a persistent knowledge store, which saw novel fraud identified in the first 48 hours, and three fraud rings in four weeks.

9.40: Despite these worries, there are also opportunities. Banks are becoming more of a lifestyle company, serving customer's financial wellness. AI plays a key role in this, Sheppard says. This is also the case with financial crime, where a holistic view of customer behaviour can be used to enhance security.

9.36: Sheppard quotes mountaineer Reinhold Messner: "It's always further than it looks. It's always taller than it looks. And it is always harder than it looks." This can be applied to the area of new technology as well! The financial services industry is changing faster than ever. Concerns include over-regulation, uncertain economy growth, geopolitical uncertainty, cyber threats and the speed of technological change.

9.33: Now we have the second opening keynote. This is looking at how to get value from AI in financial services ,and is being delivered by Gayle Sheppard, vice president and general manager of the Saffron AI Group at Intel Corporation.

9.32: How can big companies adapt? Leadbetter says that the first thing to do is transform mindsets and implement diversity. Also, you can engage with exponential organisations at the edge. Hiring a black ops team can show you how disruptors might break your company. Finally, you can partner with/acquire an exponential organisation.

9.30: There is a correlation between being an exponential company and your stock performance. There are ten attributes to being an exponential company, one of which is the staff on demand attribute. Leadbetter uses the example of Allstate, who paid US$10,000 for a Kaggle competition. This led to a 271% improvement in two weeks.

9.27: Five companies represented 50% of the growth of the US stock exchange. Leadbetter explains that these are great examples of exponential companies. TED brought in crowd engagement to become a global media company in 5 years, while Amazon worked on the Institutional Yes concept - where any new idea has to be ok'd by the boss, or turned down with a detailed two-page memo. These are both examples of massive transformative purpose.

9.23: Frameworks to adapt are mentioned. Leadbetter notes the argument that every company should think like a software company - the three-horizon framework. These are today's horizon, the next 12-18 months, and simultaneously the next 3-5 year horizon too. He brings up Facebook, and says that AI is in their third horizon.

9.20: Having carried out research into the financial services industry, Leadbetter found 10 different classifications of fintechs. He says that financial institutions need to fully understand how they should interact with these variety of fintechs, whether they should collaborate, acquire, or compete with them.

9.17: Leadbetter challenges the audience if their organisation is a linear company or an exponential company. He cites the CEO of Accenture, who said that digital is the reason that half of the companies have disappeared from the Forbes 200.

9.15: Roughly only half of the world's population has access to the internet, but this is set to change. Leadbetter notes Acquila is using efficient aeroplanes to deliver wifi coverage, while a Google project is using hot air balloons to create a wifi network.

9.11: Leadbetter cites findings that show if you apply IT to any domain, the price performance doubles every 12-18 months, no matter what happens. The researcher took that back over 100 years and found this to be the case. AI is a great example of exponential technology.

9.08: Nobody knows where the fourth industrial revolution is going, says Leadbetter, so companies needs to be flexible and dynamic, particularly in the financial services sector.

9.05: Leadbetter says that companies are changing, that rather than only thinking a couple of years ahead and being resistant to change, corporates are getting the message that disruption is coming to their businesses through the fourth industrial revolution.

9.00: Finextra's editor, Anna Milne, welcomes the assembled delegates to the event, promising a fast-paced and punchy agenda. Milne hands over to Michael Leadbetter, CEO of ExO Works. In his keynote, Leadbetter is going to discuss a 2030 vision for exponential banking.

8.25: Good morning and welcome to Finextra's live coverage of NextGen Banking London. There is a packed programme to look forward to today, scrutinising the variety of opportunities and challenges that AI poses to the financial services sector. Things get underway at 9.00, so be sure to check back here from then.

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