We can see a lot of hype about AI and Machine Learning, and its potential to transform businesses. More and more companies are adopting machine learning solutions, setting up accelerators, opening R&D centers, and investing into startups. Also, there is a large number of reports with AI market estimates and forecasts. However, it’s challenging to get the right information on machine learning development that will actually work for your business. Here are our five expert tips to make machine learning development work for you.
we continue to explore how technologies can help fintechs solve scalability challenges. We’ll try to answer the following question: How fintechs can find new revenue streams and extend their market reach? When fintechs find the technological capacity to build a scalable and reliable solution and manage to keep their operational costs low, they want to grow bigger, raise profits, and scale their business reach. However, that may be a daunting task due to strong cards of other financial services companies operating in the market.
As companies scale transaction volumes and integrate with more and more third party software, they get a growing inflow of data and services. On the downside, this subsequently increases the risk of data breaches and cyber-attacks. fintechs currently suffer from the mismatch between innovations and regulations as the latter don’t keep up with the technological advancement in the financial industry. What’s more, early-stage startups usually don’t have adequate compliance teams. Such unstable regulatory environment creates additional security and compliance challenges for the financial market players. Then how to scale up without compromising security?
To stay competitive and successful, both FinTech software developers and financial companies need to catch the waves of digital disruption and learn how to ride them right. To keep up with the finnovation pace, businesses are adopting the emerging technologies such as Data Science, AI, digital currency, Blockchain, Biometrics, and more. However, they may turn out to be intricate and present challenges you need to be ready to embrace. Here are 5 innovations FinTech software developers need to be ready to adopt to implement FinTech innovations with sense and caution.
Fintechs currently face the challenges of growing and scaling. Yet most will likely fail because: they could not find the right product-market fit, the high cost of scaling up, inability to find the right partner, and the struggle to create, launch, and quickly gain market share for a differentiated product that cannot be replicated. And to overcome those hurdles, they, first of all, need a reliable and robust platform built in compliance with the best industry practices. We outline15+ time-tested rules of fintech app development.
Specific knowledge about fintech operations is a must for efficient fintech app development that includes compliance aspects, understanding how different types of fintechs operate, background in finance and banking, and more. Also to ensure growth, fintechs need a reliable and easily scalable platform built in compliance with the best industry practices. Here are 15+ rules for fintech app developers and grouped them according to 4 underlying principles. In Part 1, we focused on security and compliance, and API-led connectivity. In part 2, we dwell on the rules related to software infrastructure scalability, and specific domain expertise.
Machine learning in finance may work magic, even though there is no magic behind it. Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. It helps reduce operational costs thanks to process automation, increase revenues thanks to better productivity and enhanced user experiences, and better compliance and reinforced security. Let’s see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology.
Are you ready to delegate making your business and legal agreements to smart contracts? Is it safe, feasible, and effective? Smart contract development offers numerous benefits as it is secure, fast, automated, and irreversible. Ethereum is one of the most popular platforms for smart contract development as it enables to solve almost any computational task. Thus many businesses across a variety of industries hire Solidity developers to build their smart contracts.
Downtime costs industrial manufacturers dozens of millions a year. Going for Big Data Analytics and predictive maintenance may be a great solution for companies that want to anticipate tech failures and slash downtime costs. It is worth noting that Big Data engineering amounts for around 70% of any Data Science project and that’s what businesses need to focus on first of all. Companies that have implemented predictive maintenance have already improved their decision-making and reduced average downtime by more than 50%.
What problems do fintechs need to solve to scale up and grow profits? They need building an easily scalable software product, partnering with other companies and engaging new customer segments, and complying with regulation and security standards while scaling up. In our series of articles we will dwell on how technologies can help you solve these 3 key challenges. We've collected and analysed findings from PWC, CBInsights, Forbes, etc., and fintech software development cases to elaborate a strategy on how to build a successful fintech business that generates profits, attracts investments and can achieve economies of scale.