BITCOIN AND CRYPTO
Article | October 26, 2020
If the history have taught us anything it’s to never trust anyone else with your digital money. That is why when dealing with digital money such as cryptocurrency or tokens you should always store it at another place from where you bought it. Coinlager is doing exactly this, focusing on what its primarily feature is – to allow customers to buy cryptocurrencies in a quick, transparent and safe manner.
Ever since Satoshi mined the first Bitcoin crypto has been on the rise. In the beginning it was more of a subculture rather than a respected currency, something which has definitely changed over the past decade.
There was one company for those of you who remembers called MT Gox that was the number 1 choice for any cryptocurrency trader between 2010 – 2013, at most the covered 70% of the Bitcoin tradable market.
In February 2014 it however came to an abrupt stop since MT Gox announced that approximately 850,000 Bitcoins had been stolen from their Hot Wallet, valued at $450 million at that time.
Even though the MT Gox hack is by far the biggest almost all of the larger exchanges have at some stage been targeted by hackers and in some cases succeeded in stealing cryptocurrencies.
“We were actually one of the early adapters to crypto back in 2011, using MT Gox as a day-trading solution to increase our assets of Bitcoin. Everything were going smoothly until the crash in 2014 where we also lost all our Bitcoins. We haven’t really spent too much time dwelling on it, but when we see that people are still relying too much on using the same company for both thee crypto and the wallet service we thought we had to do something about it. The problem has always been that seperatiing the two services have not been user-friendly – this is what we are changing, giving the customers a super easy and quick way to buy their crypto and send it over to their desired wallet.”
So what is it that Coinlager essentially does? It’s simple, we sell cryptocurrencies to the customers. Customers can register over at Coinlager in seconds and be able to purchase their favourite cryptocurrency immediately. Relying on modernised technologies we can verify the customers in real-time, giving them the chance to not have to wait for hours or even days when their account becomes activated.
It doesn’t have to be overly complicated where customers do not understand what they are buying or how much it costs. With Coinlager we show the customers A) What they are buying B) What our fees are C) Where the crypto will be sent to (customer’s choice).
If you are interested in buying crypto, come check us out here: https://coinlager.com/
If you have a question, feel free to email us at email@example.com
Article | October 26, 2020
Technological innovation over the recent decades has attracted increased interest of researchers and industry experts thereby becoming an integral part of everyday lifestyle. The challenge lies in resolving the dispute between a forward-looking innovative structure promoting innovation, and a proportionately meticulous schema that is capable of winning faith of consumer.
Information technology is fast turning to be a key instrument in the lives of consumer across generations. In current global economy, customers across industries have been pampered and the credit goes to “Bigtechs ” like Ali Baba, Apple, Amazon, E-bay, the list being exhaustive along with “Fintech ” and “Incumbent banks ” as a result, consumers expect swift product delivery with flawless service.
A review of available literature is suggestive that further expositions tend to focus on fintech and its integration in banking arena investigating the factors that underpin the choice of external partners to collaborate, design, develop and implement fintech capability while addressing the gap between research and industry evolution.
Recent developments in technology have refurbished global economies at an immensely fast pace, making the business environment extremely challenging with continued margin pressure. Digital technologies are intrusive to not only the competition but also to the role of payments in businesses impacting the ultimate consumers.
Investigating digital transformation has been of continuing interest across industries. Digitization might abolish some vital job roles, threatening the human workforce reluctant to digital changes. However, observations are indicative of focus towards higher-value tasks and creating unprecedented opportunities. For instance, adoption of digitization in financial industry, provides considerable opportunity to relationship managers to spend minimal time in operational activities and maximum towards advising customers.
Amongst many ideas laying the foundation of fintech adoption, a growing body of literature recognizes two vital causes for the evolution of fintech companies that can be routed back to a decade. Firstly, the global economic financial crisis also called economic recession that has distinctly exhibited to consumers the flaws of the traditional system. Second, the evolution of new technologies that boosted mobility, easy flow of information, speeding up the service delivery and lowering the costs.
The way banks engage the customer today has gained fresh prominence, with a movement from branch banking to digital systems, benefitting from customer insights. Aiming to enhance consumer engagement and gain competitive advantage, debates continue about the best strategies banks adopt to engage with customers that has resulted in adding capabilities and complex technology on top of systems and processes to meet dynamic customers’ expectations to gain real time personalization.
Much debated question is whether organizations with traditional framework are able to come up to such expectations becoming capable of disrupting industry by prompt digital delivery using advanced algorithms and digital platforms to successfully provide unrestricted access to information bits. Promising superior experience to users, however, if industry experts hit the bulls-eye and tend to offer more competitive prices, enhanced operational controls may entitle lesser risk and probability of higher revenues.
Such developments bring along advantages and disadvantages at the same time. Whereas advantage lies in reduced transaction processing times, service excellence and global integration the disadvantages lie in the fact that not many users are keen to shift to the fintech modes as far as their financial transactions are concerned since they are apprehensive of the risks associated with such adoption, witnessing this paradigm shift in the pace at which industry is developing focussing on much saturated red ocean of retail banking and gradually making a shift towards payment systems, which seems to be an untapped blue ocean of opportunities.
The underpinning factors that govern the financial industry are KYC / AML and CFT together forming the basis for regulatory controls. Ethical transparent business knit together with service excellence, minimal risk, a strong regulatory framework in the competitive industry has given the incumbent banks an opportunity to partner, collaborate and codevelop with technology and consulting firms for collaborative innovation.
Technologies like artificial intelligence, blockchain are capable of providing effective product suite to clients resulting in scalability of business.. On the other hand, robots replacing front end customer interface causes threat of redundancy to human capital and increase the training costs. As a result, many times an informal approach to collaboration tends to delay the outcome since the senior management looks at digitization in transaction banking as a profitable step and the middle management is hesitant of human redundancy, training etc which might cause delay.
Taken together, a probable explanation advocates that common goals of digitization in financial institutions are regulatory control, risk mitigation, increase in revenue and to meet dynamic customer expectations by co-developing with external partners to gain competitive advantage. This adoption may further lead to higher cohesiveness in departments, improved value chain and reduced turnaround time with higher resolution quality.
The digital transformation is capable of reducing the operational costs and overheads leading to increased profits, improved efficiency, better regulatory controls with less risks and collaborative opportunities for partners taking benefit of its tech talent to reach desired results.
Anikina, I.D., Gukova, V.A., Golodova, A.A. and Chekalkina, A.A. 2016. Methodological Aspects of Prioritization of Financial Tools for Stimulation of Innovative Activities. European Research Studies Journal, 19(2), 100-112.
Boston Consulting Group 2018, Three Keys to successful digitization in Transaction Banking, Boston Consulting Group, pp1-2
Botta, et.al.,2016. Technology innovations driving change in transaction banking. [Online] Accessed on March 11, 2018 Available at:https://www.mckinsey.com/industries/financial-services/our-insights/technology-innovations-driving-change-in-transaction-banking
Capegemini & LinkedIn, 2018, World Fintech Report, p. 9-10
Hammond, Alex, August 2017, How banks are getting the digitization of core banking wrong, pp 1-8, [Online] [Accessed on April 02, 2018] Available at:https://www.bobsguide.com/guide/news/2017/Aug/23/how-banks-are-getting-the-digitisation-of-core-banking-wrong/
Johnson. et. al., 2017, Exploring Strategy chapter 3 Industry and sector analysis, pp. 62-91
Markovitch, Shahar & Wilcott, Paul, May 2014, Accelerating the digitization of business processes, Digital McKinsey, McKinsey & Company, pp 1-5
Mehrotra, Mohit, 2014, Digital Transaction Banking: Opportunities & Challenges, Deloitte Consulting Pte Ltd., pp 1-22
Olanrewaju, Tunde, July 2014, The rise of the digital bank, Digital McKinsey, McKinsey & Company, pp 1-5
Puschmann, Thomas, 2 February 2017, Fintech, Bus Inf Syst Eng 59(1): 69-76, Springer Fachmedien Wiesbaden 2017
Saksonova, Svetlana, Kuzmina-Merlino, Irina, 2017, Fintech as Financial Innovation – The Possibilities and Problems of Implementation, European Research Studies Journal, Volume XX, Issue 3A, pp 1-14
Article | October 26, 2020
The world is digitizing, and the world is digitizing because we’re seeking low friction and immediacy. We want immediate responses; we want stronger commerce connections that can scale up to more rapidly. So, within that framework, one can’t expect banking and financial services to stay the same as it has been, because ultimately it has to shift.
Artificial Intelligence is bubbling with a lot of energy at the moment, and so is Fintech. There has been a lot of investment going on in it, and it’s under so much spotlights. The rate of innovations and the abundance of new technologies have sprung up everywhere. Things from artificial intelligence, peer to peer lending, big data, block chain, crowd funding, digital payments, and Robo advisors, just to name a few.
We need to think about FinTech with two capitals T’s that is, TECHNOLGY and TRANSPARENCY. It’s more about technology, enabling the banking industry to do the wonder, and Transparency because it’s a sector where customers can make much more informed choices. But what has made Fintech go so unmask is just the pace of innovations in this space. FinTech has now moved from prevention to resilience. We are just at the tip of the iceberg.
Globally, the value of an investment in Fintech companies amounted to approximately 112 billion U.S. dollars in 2018, which was a record high for the sector. The annual value of global venture capital investment in Fintech companies is increasing and doubled between 2017 and 2018.
This is an industry that is hungry for change because the consumers are hungry for change, and so the big corporations, the incumbents are also ready to change. Consumers want seamless, frictionless experiences. They want all the pain points removed from their banking journey.
Table of Contents
• Artificial Intelligence- Paving the Way for the Future in Banking
- Embracing Conversational AI in Banking
- Driving Personalization in Banking through Artificial Intelligence
- AI-Model for Automated Credit-Scoring and Loan Processes
- Transforming Wealth Management with AI
- Utilizing Robotic Process Automation Software in Banking
• In Conclusion
Artificial Intelligence- Paving the Way for the Future in Banking
Artificial Intelligence has the potential to revolutionize how consumers and businesses handle financial transactions. There will surely be hits and knocks along the way, but AI is not going away anytime soon. It is the future.
FinTech companies want to deliver personalized and cost-effective finance products. To do so, they need to utilize large numbers of data from various touch-points. Introducing the financial sector with advanced techs like big data, artificial intelligence, and blockchaincan facilitate banking and finance go far beyond cashless payments and mobile services toward personalized customer experience that will transform FinTech in 2020.
Financial institutions now know their customers' behavior and social browsing history. The accelerated rise of Artificial Intelligence and machine learning has resulted in banks being able to reduce the number of operations as they embrace the power of automation. Artificial Intelligence facilitates real-time omnichannel integration of these insights to deliver a personalized one-to-one marketing experience for their customers.
AI’s potential can be looked at through versatile lenses in this sector, especially its implications and applicability across the operating landscape of banking.
Learn more: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/financial-services/deloitte-uk-world-economic-forum-artificial-intelligence-summary-report.pdf
The three main channels where banks can use artificial intelligence to save on costs are front office (conversational banking), middle office (anti-fraud) and back office (underwriting). Let’s explore more on how banks can use Artificial Intelligence to constantly innovate at scale:
Embracing Conversational AI in Banking
An artificial intelligence feature that is redefining customer engagement is conversational AI. It has been viewed as a cost-effective way to interact with customers. Nowadays, conversational interfaces represent one of the biggest shifts in banking user interfaces to date and are modifying how they obtain and retain customers and enhance their brand identity.
According to a study conducted by Juniper Research, chatbots can save at least 4 minutes of a customer service agent’s time. While saving 0.70 USD per query, in the process. Conversational AI has now become the preferred solution for productive customer communication among banks.
The universality of messaging apps, like Facebook Messenger, WhatsApp, Slack, Microsoft Teams or SMS, and the adoption of voice-activated assistants such as Amazon Alexa, Google Home, or Apple’s Siri are bringing conversations back into our banking experiences.
Conversational Banking Experience
For example, the Swiss bank UBS partnered with tech giant Amazon to merge its “Ask UBS” service with Amazon Echo. Customers can communicate with multiple banking processes, via the chat interface, such as reporting potential fraud on their banking cards, applying for an increase on their credit card limit, or getting a breakdown of their recent transactions, and more.
Driving Personalization in Banking through Artificial Intelligence
Customers need banking on the go. They are looking for more personalized experience and expect to transact with banks from the convenience of wherever they are. Data advises that businesses that offer personalized services achieve far better business outcomes. Giving the right individual experience through the right channel at the right time can make banking more personalized. AI can play a significant role in assisting banks to understand customer behavior by leveraging transactional and other data sources.
The Boston Consulting Group has estimated that a bank can garner as much as $300 million in revenue growth for every $100 billion it has in assets. All by personalizing its customer interactions.
• Artificial Intelligence enables banks to customize financial products and services by adding personalized features and intuitive interactions to deliver meaningful customer engagement and build strong relationships with their customers.
• Artificial Intelligence enables a higher degree of personalization and customization by tapping into information such as customer behavior, social interaction, and even health or important event dates, all to create a well-rounded picture of their customers’ profile.
• AI can classify prospects based on financial capability, family size, etc. and offer tailored products.
To carry out extensive personalization projects, banks are looking to collaborate. They’re now teaming up with fintech and software corporations to provide technological capabilities they do not maintain.
In 2019, the total value of transactions in the personal finance segment will amount to $1,092,496 million according to Statista. Remarkably, the market’s largest segment is robo-advisors, with total assets under management of $980,541 million. In 2023, the number of people using robo-advisors is predicted to be 147 million.
Organizations like Optimizely, Braze, and Crayon Data offer the financial sector the means to personalize the customer experience. Crayon’s proprietary AI-led recommendation engine, maya.ai, allows banks to create personalized digital experiences for their customers. All that with the help of machine learning algorithms.
AI-Model for Automated Credit-Scoring and Loan Processes
Artificial intelligence not only automates menial and repetitive tasks. It can be trained to take business decisions that normally require a specific level of cognitive thinking. Lending and credit scoring are the critical business for banks and directly or indirectly touches almost all parts of the economy.
Banks always relied on models and experts to make effective credit decisions. Now models are becoming sophisticated enough to replace experts. Banks and credit scorers are employing machine learning models to track customers’ credit records. And make well-informed decisions on loan approvals.
Banks and credit scorers are employing machine learning models to track customers’ credit records and data. And make well-informed decisions on loan approvals. The AI-based credit scoring model can score potential borrowers on their ‘creditworthiness’ by factoring in alternative data. The more data available about the borrower, the better you can assess their creditworthiness.
This data could include candidates' social media/internet activity and websites visited and online purchases history. By examining the online behavior of a borrower, these models can predict the most credit-worthy candidates for loans. And also predict who is most likely to back out.
In the new digital reality, AI-powered credit decision permits lenders to:
• Fast and secure loan origination process
• Automate borrower`s digital journey
• Find and filter unfit borrowers based on sophisticated proprietary models powered by deep neural networks
• Lessen the operational costs of origination
• Authorize unhindered scalability of the lending business
Transforming Wealth Management with AI
Wealth managers are positively deploying artificial intelligence (AI) to answer the needs of a new generation of tech-savvy high net worth individuals.
According to the 2018 Asia-Pacific Wealth Report (APWR) released by Capgemini, the APAC region witnessed a 12.1 percent growth in HNWI population in 2017, and a 14.8 percent rise in wealth, with the region, now forecast to exceed US$42 trillion by 2025.
One of the AI trends in wealth management is the potential for the technology to move beyond traditional tasks, such as KYC and risk management, to new centers of enhancing relationship management and client experience.
On the one hand, firms are investigating how they can make their relationship managers more productive. On the other, the new generation of clients wants predominant online services, assisting banks to examine how they can optimize their digital offerings.
“Consumers’ and SME’s behavior and needs are changing fast,” said Rosali Steenkamer. There is an immense data explosion with structured and unstructured data. Only big data-driven models, Machine Learning algorithms and Artificial Intelligence can tackle this to serve the right solution to the right customer. Traditional technology is simply not able to deal with these challenges.
-CCO and Co-Founder at AdviceRobo.
Relationship Managers are not motivated to capture datasets. The only solution is to encourage the front office to collect new data, as well as collaborate with colleagues who develop AI-powered products and services. Doing this will drive productivity for Relationship Managers and an enriched experience for their end clients.
Everyday tasks can be handled by AI systems, releasing wealth managers to concentrate on higher-level investment strategies. AI systems can also analyze client data to adequately create packages prepared for specific financial and social demographics. Utilizing AI in finance expands service offerings while also making them more customizable. With a variety of AI tools at their disposal, wealth managers are outfitted with the research and data insights essential to make quicker, more informed decisions for various clients.
Learn more: https://capital.report/blogs/how-fintech-is-shaping-the-future-of-wealth-management/8244
Utilizing Robotic Process Automation Software in Banking
This year robotic process automation (RPA) will continue to impact financial institutions, to help them be more efficient and effective, as well as help ensure they meet federal and state compliance requirements.
RPA is growing rapidly. Recent RPA trends and forecasts anticipate that the market for robots in knowledge-work processes will reach $29 billion by 2021. For the banking industry, robotics outlines a unique and underutilized way to increase productivity while minimizing traditional repetitive and manual-labor-intensive processes.
The accelerated rise of AI and machine learning has resulted in banks being able to reduce the number of operations as they embrace the power of automation. AI facilitates real-time omnichannel integration of these insights to deliver a personalized one-to-one marketing experience for their customers.
So, when we look at these phases of development in the Banking Industry, we understand that it’s not just about inserting technology into banking; there is a larger shift here. Part of the shift is around trust and the utility of the bank. Artificial intelligence and machine learning technologies allows banks to turn vision into reality. Whether you are ready for it or not the AI revolution is poised to provide exciting avenues for innovations.
Article | October 26, 2020
There’s no doubt that the Fintech software development industry has attracted a lot of attention from consumers and investors alike. In Finance, Fintech is synonymous with convenience, innovation, and accessibility. With the enormous solutions that Fintech promises to offer, it’s no wonder venture capitalists are willing to put their money in Fintech startups.