Every now and then, FinTech service providers approach Product Managers seemingly insulted by the question “have you considered amplifying your DevOps team or optimizing your Cloud Strategy with outside help to scale faster and cheaper?”.

Convincing these naysayers can sometimes make you feel like you are assuming a “bad cop” parental role – i.e. becoming someone who knows what’s good for you based on extensive experience, even when you may not see it or believe them at first. So let’s jump right into the spiel.

There are three imperative considerations to implementing a “Buy, Build, Partner” strategy that rest on the premise (which Silicon Valley seemingly forgets from time to time) that time and material resources are finite: Leverage Open-source, Protect your Mindshare and Trust Inorganic Growth. 

Steve Jobs once said, “it doesn’t make sense to hire smart people and tell them what to do; we hire smart people so that they can tell us what to do” –  Even for Apple, a maximum-security IP fortress, this did not mean augmenting payroll. In 2012, Apple revealed its 15-year association with the likes of Infosys and Wipro, implying Apple’s journey to a market cap of 1 trillion dollars was not achieved by internal hiring alone. The good news is, this is only getting easier to achieve, the bad news is too few emerging names are following best practices to accelerate ahead. Instead, the C-Suite is often at the mercy of the apprehension and fear of internal (usually technical) “gatekeepers”. Let’s take a look at 3 key components of a “Buy, Build, Partner” strategy in 2020, which assumes your partners and service providers aren’t seeking multi-year/million-dollar engagements, that they adapt to your servicing time zone with suitable SLAs and that they will not compromise on quality. 

1.The ubiquitous existing Open- source tech stacks today do not require you to reinvent the wheel

• The famous four (Apple, Google, Microsoft, and Linux) have now been replaced with robust community-driven Open-source code that is not dependent on cyclical patches, provides real-time bug fixes and new enhancements, and can deliver results in T-0 vulnerabilities as opposed to proprietary solutions. Embracing the world of Open-source and applying your domain knowledge is how you get the biggest bang for your buck. 
• For instance, Rasa: Open- source Conversational AI, has given multiple verticals (Airline, Retail, Healthcare, Financial Services, and counting…) the opportunity to create enterprise-grade intelligent virtual assistants that are well versed in the context of their industry. 

Extrapolate this tangible product approach to AI/ML solutions, data visualization, testing, cloud strategy and platform engineering with a range of emerging tech stacks such as Kubernetes, ELK, Kibana and Terraform. Take your pick and get in touch with me if you are looking to explore!

2. Protect your mindshare, build responsibly, keep costs low and hire for what you don’t know

• Keep your team the right size and working on exciting stuff such as product development and feature building. A service provider with domain experts can easily handle manual QA, DevOps and migration projects that can be executed without the overhead (read: large fixed costs such as offices, servers, inventory) of doing it on your own. Note again for anxious readers: IP is not at risk, especially if your contract specifies that source code is to be handled and maintained by your firm. Decide on an outcome-based model that establishes clear deliverables. 
• Burn rates should not make investors or leadership teams uncomfortable. As Venture Capitalist, Mark Suster warns, “a company’s runway should not fall below 6-7 months of cash on hand” and reminds us “high fixed costs and high debt rates killed many great companies in Dot Com 1.0”. Figure out a “Buy” strategy to keep sticky situations and rainy days to a minimum by increasing variable costs. This in turn, generates momentum for speed to market and allows you to maintain a position well ahead of your peers. 
• Even though we are inundated with “self-help” advice on how to manage our personal lives and relationships, institutional introspection is underrated. It is equally just as important to identify and diagnose weak areas in your company from the outset. Then buy or hire those services from a vendor that spends day and night perfecting that exact skillset. 

3.The Butterfly Effect of Partnering on Business Development 

• Do not underestimate the “Butterfly Effect” of your outsourcing partner’s ability to drive inorganic growth in unique ways. Unsuspecting partnerships have helped drive:
  • Geographical scale
  • Customer acquisition and adoption
  • IP augmentation 
  • Insights & analytics 
• Choose the domain experts that can connect you to peers and establish these relationships. Just how Wipro and Infosys were able to leverage its internal IT projects with Apple to amplify adoption. Sewing a web of interconnectivity of Apple products with other clients’ business applications and adapting best practices continue to be a win-win for the iPhone/iPad maker as well as their outsourcing providers. 

Finally, the skeptics are not wrong to be wary of anything except “Build”. It  has become the dominant fall back approach for many emerging technology companies across Retail, Healthcare, FinTech and Blockchain after “outsourcing” earned a bad reputation over the last decade (read: overcharging, “landing and expanding”, and poor results). However, with the right governance, acknowledging that most engagements can leverage free open-source solutions with effective domain-specific frameworks, and creating equitable partnerships, a little bit more of “Buy” and “Partner” can get you where you want to go exponentially faster. 

According to ‘The Pulse of FinTech 2018’ report by KPMG, fintech startups bagged over $111 billion in investments across 2,196 deals. The technological evolution of fintech startups has outmatched that of traditional financial services by many leagues. Not only has this served to disrupt the space by directly pitting startups against tech giants, but has also transformed the tools of global trade and commerce.  One startup even estimated the total cost of the recent US government shutdown right down to its last cent.

Various emerging technologies have given rise to new business-technology startups that didn’t even exist ten years ago. It’s no surprise then that investments in sectors of regulatory technology (RegTech) have tripled from USD 1.2 billion in 2017 to USD 3.7 billion in 2018.

Meanwhile, the versatile nature of blockchain technology is being used to craft specific solutions for capital markets, everything from cryptocurrencies to capital issuance. Even the simplest technology tools in the hands of FinTech are being used to enhance point-of-sale customer experience while also controlling fraudulent transactions.

However, the most recent breakthrough amongst all these has been the rise of FinTech startups in capital markets. Since 2010, capital market infrastructure (CMI) linked FinTechs has grown nearly 300% since 2010, offering solutions to tackle complex front, middle, and back-office problems.

Why Startups?

For startups, success amidst cut-throat competition isn’t easy to achieve. ‘Nine out of ten startups fail’ is an oft-repeated maxim. Compliance and legal issues, along with inadequate funding have been the primary roadblocks in this quest. But despite these difficulties, fintech startups are ideally placed to resolve longstanding issues in the capital markets industry. These issues include high structural expenses, stagnant revenues, and enormous capital costs.

These challenges combined with the changes demanded by regulators have led to a decline in the returns on equity (ROE) for investment banks year after year. CMI providers (CMIPs) are compelled to deliver regulatory changes, such as the shift toward compulsory central counterparty clearing of over-the-counter derivatives, or external changes in customer behavior within the investor scenario. These pressures and complexity typically combine to cause organizational fatigue. This leaves high-level management with hardly any scope to invest in initiatives that can increase ROE.

Costs associated with the development and implementation of regulatory compliance systems are unavoidable, but costs incurred by investment banks to maintain disparate systems are unnecessary. Despite wanting to harness cutting edge technologies, they get caught up in the devil’s snare of legacy infrastructure. Instead, they need to leverage an external fintech solution to achieve their goals more optimally. Since startups aren’t tied to any entrenched IT architecture, they can accelerate cutting-edge product and service development.

The agile infrastructure of fintech startups has been proven to improve productivity by 25 to 30% within 6 to 18 months. CMIPs are already being empowered by fintech startups towards solving many of their challenges and are poised to make a significant impact on the capital markets industry. What they are not as certain about is knowing which specific technologies hold the key to helping them most efficiently resolve their challenges and the best collaboration methods when working with fintech firms.

Balancing the Equation

Sopnendu Mohanty, the Chief Fintech Officer at the Monetary Authority of Singapore (MAS), stated that while we normally understand fintech as a technology firm performing banking activities, the reality is only a fraction operate within the banking segment. Most startups are assisting in the digitization of banks. And while mergers and acquisitions by larger firms have been thought to benefit startups, recent developments along the CMI value chain suggests quite the reverse. 

Most startups assist banks by modernizing their dated infrastructure by becoming vendors and partners. Alliances such as the one between ING and the automated lending platform Kabbage are proof that conventional banks are looking to present new offerings to their customer base, and move to a more streamlined, agile, ‘plug-and-play’ model. 

They will continue to drive greater productivity in post-trade services like regulatory reporting and risk management by deploying automation and robotics. We are already witnessing capital markets seeking our next-gen artificial intelligence solutions to cope with their growing data streams and blockchain to optimize their transaction exchanges. Startups are well-positioned to bring in new digital markets, serve as an alternative to conventional access to capital and enhance the security of global financial systems.

Making Finance Relevant

The disruption brought about by fintech startups is indicative of the agile, mobile-first approach that customers across most sectors want. For the record, smaller startup fintech companies are the most active in the CMI space. Despite their considerable data pools and comprehensive resources, technology giants are being given a run for their money by these startups due to their enhanced agility and lack of legacy burdens.

They operate with existing providers rather than against them, and most of their products act as components within the industry, making conciliation much easier. Fintech startups have also been heavily backed by venture capital investment from the CMI sector and this trend is also on the rise. “The Fintech 250” list of 2018 by CBInsights’ further reinforces this reality, with Kabbage, incidentally, being the best-funded fintech startup under business lending and financing.

Ultimately, fintech startups are defying the norm by creating a space for established financial giants to leverage new technologies in a way that will bring about radical but meaningful change. There is no denying that fintech companies will continue to pioneer and outpace traditional financial giants as their technological innovation brings an unparalleled depth of value for capital markets in the 21 st century.

A New World of Banking

The rise of fintech in the last half decade or so has taken the financial world by storm. Research suggests that there are now more than 7,500 fintech firms around the world which have raised nearly USD 109 billion in investment. The sector raked in a record breaking USD 54 billion investment in 2018 and USD 10 billion within the first quarter of 2019. 

Clearly, the hunger for fintech is growing, and with it, the fear among banks and traditional financial business about potentially lost revenue and customers. The fact that customers are increasingly preferring these non-traditional competitors does little to calm the uncertainty. 

As established players in the financial services industry wake up to this new business dynamic, the majority are attempting to collaborate with fintech: to leverage its ever-expanding ecosystem, turn the innovation to their favor, and address the concerns that arise with their business being at risk. Research reveals that as many as 82% of incumbents in the financial industry sector expect to enhance their partnerships with fintech players, going forward. 

Fintech – A Force to Reckon With

Fintech can be rightly characterized as a movement that has brought disruptive and transformative innovation in financial services through cutting-edge technology. Unlike traditional financial institutions, fintech startups have the advantage of not being burdened by age-old regulatory constraints, legacy systems and processes. This has allowed them to move faster and come up with solutions that compete directly with conventional methods of financial service deployments. 

Another aspect that has fuelled the rapid progression of fintech is an entirely new generation of well-informed and connected mobile consumers who continue to reshape financial service requirements. With time, fintech companies have managed to rope in these digital natives with smart banking platforms. This has given them a head start in the race to capitalize on the ‘1.7 billion billion adults, who according to World Bank’s Global Findex Database 2017 are naturally inclined towards smart fintech services.

On the other hand, major players in the financial services sector and capital market incumbents have failed to gain precedence on this front. Burdened with massive structural costs, hefty capital charges, and stagnant revenues, this sector continues to score low on the innovation index. Additionally, the relentless pressure to stay compliant and adhere to regulatory guidelines also leaves organizations short of bandwidth to invest time and resources in initiatives that can improve margins. 

There’s no denying that in the digital age, customer experience (CX) is the final battleground for businesses. And here, fintech has a natural advantage. By placing CX above everything else, fintech offerings have been able to provide their users with unending benefits. For instance, by leveraging smart application program interfaces (APIs), fintech companies are able to nurture a healthy community of third party partners around their native software problem. Open APIs allow fintech players to expand their customer services by enabling third party partners and developers to create their own apps and layers into the middleware. 

Apart from this, the algorithmic design and data-rich environment in this sector has proven ideal for machine learning (ML), artificial intelligence (AI) and blockchain-driven product deployments. Developers today are able to leverage these technologies to simplify and optimize cumbersome and effort-intensive processes such as compliance, credit checks, risk management, and P2P payments. 

But there’s good news. These technologies can yield similar results in capital markets as well, provided they are strategically implemented in the right areas. For instance, process automation with Robotic Process Automation (RPA) can help organizations working in the capital markets space replace manual legacy systems, make the systems compliant with Know Your Customer (KYC), Anti-Money laundering (AML) and other regulations, reconcile reports and connect middle and back office functions. On the other hand, more contemporary technologies like AI can simplify cumbersome processes such as trade settlement, compliance reporting, contract management and accounts payable. 

Blockchain, is another area which promises to yield unprecedented gains for capital market players. No wonder, the financial services industry has witnessed some of the biggest use cases of this technology. For example, in digital trading, blockchain is helping organizations reduce settlement times. In the current trading architecture, a single transaction can take days to settle. A blockchain-based settlement solution significantly curbs this turn-around time. A cryptocurrency token that serves as a proxy to a particular transaction is immediately transferred to the wallet of the beneficiary, confirming the completion of the settlement and ledger update.   

Extrapolating into the Future of the Financial Services Sector

With the gradual implementation of next-generation technologies like ML, neural networking with long/short term memory, Blockchain,  AI and robo-advisors, fintech will continue to gain trust and popularity among customers . 73% of millennials are eager to shift to a new financial paradigm where service products from technology companies like Google, Apple, Paypal, and Amazon are more exciting, intuitive, and CX-friendly than anything traditional financial players currently provide.   

The times are clearly changing. Fintechs are fast opening the virtual vault doors to innovation in the once impenetrable banking and the financial services sector. Can traditional players take the bold steps necessary to match the frictionless experience that’s the new norm, or will they eventually lose grounds to the new entrants? Only time can tell. 

Machine learning is one amongst those technologies that is invariably around us and that we might not even comprehend it. For instance, machine learning is employed to resolve issues like deciding if an email that we got is a spam or a genuine one, how cars can drive on their own, and what product someone is likely to purchase. Every day we tend to see these sorts of machine learning solutions in action. Machine learning is when we get a mail and automatically/mechanically scanned and marked for spam within the spam folder. For the past few years, Google, Tesla, and others have been building self-drive systems that may soon augment or replace the human driver. And data information giants like Google and Amazon can use your search history to predict which things you are looking to shop for and ensure you see ads for those things on each webpage you visit. All this useful and sometimes annoying behavior is the result of artificial intelligence.

This definition brings up the key component of machine realizing specifically, that the framework figures out how to tackle the issue from illustration information, instead of us composing a particular rationale. This is a noteworthy advancement from how most writing of computer programs is done. In more customary programming we deliberately examine the issue and compose code.

This code peruses in information and utilizes its predefined rationale to distinguish the right parts to execute, which at that point creates the right outcome.

Machine Learning and Conventional Programming

With conventional programming, we use code structs like– if statements, switch-case statements, and control loops implemented with — while, for and do statements. Every one of these announcements has tests that must be characterized. And the dynamic information, typical of machine learning issues can make defining these tests very troublesome. In contradiction to machine learning, we do not write this logic that produces the results. Instead, we gather the information we need and modify its format into a form which machine learning can use. We then pass this data to an algorithm. The algorithmic program analyses the data and creates a model that implements the solution to solve the problem based on the information and data.

Machine Learning: High-Level View

We initially start with lots of data, the data that contains patterns. That data gets inside machine learning logic and algorithm to find a pattern or several patterns. A predictive model is the outcome of the machine learning algorithm process. A model is typically the business logic that identifies the probable patterns with new data. The application is used to supply data to the model to know if the model identifies the known pattern with the new data. In the case that we took, new data could be data of more transactions. Probable patterns mean that a model should come up with predictive patterns to check if the transactions are fraudulent.

Machine Learning and FinTech
FinTech is one of the industries that could be hugely impacted by machine learning and can leverage machine learning technologies to get better predictions and risk analysis in finance applications. Following are five areas where machine learning could impact finance applications and so financial technologies can become smarter to take care of fraud detection, algorithmic trading or portfolio management.

Risk Management
Applying predictive analysis model to the huge amount of real-time data can help the machine learning algorithm to have command over numerous data points. The traditional method of risk management worked on analyzing structured data against some data rules which were very constrained to only structured data. But there is more than 90% of data that is unstructured. Deep learning technology can process unstructured data and does not really depend upon static information coming from loan applications or other financial reports. Predictive analysis can even foresee the loan applicant’s financial status that may be impacted by the current market trends.

Internet Banking Fraud
Another such example could be to detect internet banking fraud. If there is a continuous fraud happening with the fund’s transfer via internet banking and we have the complete data, we could find out the pattern involved. Through this, we can identify where are the loopholes or hack prone areas of the application. So, it’s all about patterns and predicting the results and future based on those patterns. Machine learning plays an important role in data mining, image processing, and language processing. It cannot always provide a correct analysis or cannot always provide an accurate result based on the analysis, but it gives a predictive model based on historical data to make decisions. The more data, the more the result-oriented predictions that can be made.

Sentiment Analysis
One of the areas where machine learning can play an important role is sentiment analysis or news analysis. The futuristic applications on machine learning can no longer depend upon the only data coming from trades and stock prices. As a legacy, the human intuition of financial activities is dependent upon trades and stocks data to discover new trends. The machine learning technology can be evolved to understand social media trends and other information/news trends to do sentiment or news analysis. The algorithms can computationally identify and categorize the opinions or thoughts expressed by the user to make predictive analysis. The more the data the more accurate would be the predictions.

Robo-Advisors
Robo-advisors are a kind of digital platforms to calibrate a financial portfolio. They provide planning services with least manual or human intervention. The users furnish details like their age, current income, and their financial status and expect from Robo-advisors to predict the kind of investment they can make, as per current and futuristic market trends to meet their retirement goals. The advisor processes this request by spreading the investments across financial instruments and asset classes to match the goals of the user. The system works on real-time modification in user’s goals and current market trends and does a predictive analysis to find the best match for the user’s investments. Robo-advisors may in future completely wipe out the human advisors who make money out of these services.

Security
The highest concern for banks and other financial institutions is the security of the user and user’s details, which if leaked could be prone to hacking and eventually resulting in financial losses. The traditional way in which the system works are providing a username and password to the user for secure access and in case of loss of password or recovery of the lost account, few security questions or mobile number validation is needed. Using AI, in the future, one can develop an anomaly detection application that might use biometric data like facial recognition, voice recognition or retina scan. This could only be possible by applying predictive analysis over a huge amount of biometric data to make more accurate predictions by applying repetitive models.

How Can Magic FinServ help?

Magic FinServ is aggressively working on visual analytics and artificial intelligence thereby leveraging the concept of machine learning and transforming the same in the perspective of technology to solve business problems like financial analysis, portfolio management, and risk management. Magic FinServ being a financial services provider can foresee the impact of machine learning and predictive analysis on financial services and financial technologies. The technology business unit of Magic uses technologies like Python, Big Data, Azure Cognitive Services to develop and provide innovative solutions. Data scientists and technical architects at Magic work hand in hand to provide consulting and developing financial technology services having a futuristic approach.