Article | March 23, 2020
The wealth management industry can no longer ignore the rise of fintech. Investors have pumped more than $100 billion into the fintech market since 2010—including $6 billion in the first quarter of 2019. Those investments are going toward things like robo-advisors and investment apps that help Millennials streamline their personal investments.I first noticed the fintech trend about a decade ago. Now, I can see that fintech is shaping the future of wealth management.
Article | March 17, 2021
In the ongoing technological era, the outsourcing software development company transports their valuable resources to their core business model and helps businesses to save a lot of time and money. To survive the digital era for financial institutions helps to drive efficiency in Fintech companies. Moreover, software development outsourcing companies offer solutions that enable financial institutions to manage bulk data, tech products, and much more to build an efficient environment with their tech services.
The financial service industries are becoming a leader in software development and corporate new strategies for fintech businesses. Many companies are planning to use this working model that is specialized in all processes and seek more agility and quality.
Outsourcing software development to the fintech industry can bring many gains to companies, so, in this blog we’re going to look at them closely. According to research, investments in fintech are expected to reach $40 billion in 2021. Additionally, software outsourcing companies makes the development process smooth and improves the quality of data analytics in the industry.
Read on to know the benefits of outsourcing software development to the fintech industry and achieve the dedicated digital transformation goals of your business.
What Are The Benefits Of Outsourcing Fintech Software Development?
Expand your business development
The benefits of outsourcing software development services creates a pleasing and commending condition for the fintech industry. It becomes easier to execute actions and plan for the company’s expansion if the management focuses on the core business and internal quality processes.
Additionally, the development service provider also helps to sustain the growth of the operation without investing huge investments in infrastructure and technology.
Economies of scale
If you’re planning to internally develop a software application requires both money and time. And not everyone has an extended IT team with dedicated developers. In such situations, software development outsourcing companies become your partner. The services provided by software outsourcing agencies for fintech development helps businesses to achieve economies of scale and allow them to invest more time on core competencies and perform critical tasks.
However, software development outsourcing offers competitive advantages to fintech industries by minimizing costs, better customer service, and maintaining product quality with an affordable budget.
Optimize time for dedicated professionals and managers
Hiring outsourcing software development services helps businesses a practical and sensible optimization of the time of professionals and managers. If your business is planning to enhance the process of professionalism.
To such a degree, fintech industries can directly help to increase the capital and implement strategic tasks and analyze surveys that collaborate to make the right decisions. Thus, outsourcing software development helps to monitor indicators and supervise the possibility of risk to the company.
Risk Management/ Critical-path method
IT outsourcing software development service providers carry a lot of risks. So to grab the absolute advantages, an organization should build effective risk management plans.
Reducing the number of data breaches is one of the most important challenges faced by many fintech industries. This challenge is even more critical when you consider the information type such as salaries, credit card information, social security numbers, and much more which can be used by criminals for gaining profit. Various components and operations help to reduce risks and provide a successful path for outsourcing when you know the challenges and tackle the situation.
Increased profits through data analytics
Finance is the leading data collection and analytics industry that helps to increase software profit via data analytics. Popular investment banks like Goldman Sachs and JPMorgan have employed specialists who analyze data when issuing trading futures.
These fintech software development industries are now identifying customer data and helping them to increase sales and promote customer loyalty. To analyze creditworthiness and provide services to each customer they use credit scores and demographic data that also helps them to build analytics software with the Python programming language.
Reduce software server load via cloud computing
To implement cloud computing technology, the fintech industry has been reluctant to use web-based storage for bulk data which is vulnerable to hackers. There are few improvements in data security that have led banks to begin integrating modern technology into their core business functionalities.
To store sensitive business information related to accounting and communications, many banks are using cloud computing. In recent years, it is reported that the SaaS model is one of the best models that is implemented by fintech software enterprise to store emails, contact lists, and other important information online.
Faster product development
Hiring software outsourcing companies helps fintech industries to start your projects quicker and complete them before the deadline. The outsourcing enterprise implements traditional hiring processes for software development projects and instructs those dedicated developers to perform critical tasks who can start product development without investing much time on the hiring process.
The dedicated team of developers working at any outsourcing companies help to shorten the fintech software development lifecycle and work on multiple projects to quickly resolve common problems and reduce the overall length of the software.
The fintech enterprise is investing millions of dollars to build custom software according to their business requirement to survive in the new data-driven marketplace. Leveraging a fintech software development service is much bigger than just launching a system that enables the creation of innovative fintech solutions in the future.
Many of these companies are switching to fintech software development that provide a dedicated development team to perform critical and complex tasks of any software and reach their goal without distracting from their core missions.
Article | April 6, 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 | February 24, 2020
This blog attempts to answer the following key questions at a high level: What could financial technology companies (fintechs) do to enable greater consumer or SME (customer) adoption of their products and services beyond early adopters? What could fintechs do to pull under-served and well-served customers away from incumbent banks and established providers?