Commentaries and insightful analyses on the world of finance, technology and IT.

August 17, 2016

The 'artificially intelligent' hedge fund rises

- By Sweenie Dabas and Mayur Bansal

Right now, artificial intelligence (AI) is certainly the buzzword in financial services. From robo-advisors (automated financial advisors and planners) and humanoid robots in branches, to chat-bots (chat robots for customer service) and machine learning algorithms for fraud detection and credit scoring, AI is inundating the world of banking, payments, and wealth management. And the big, secret world of hedge funds is actively exploring this technology as well.

The traditional systematic trading techniques used by hedge funds have to rely on human beings to develop the mathematical models to find trading opportunities. Now, AI is enabling the development of self-learning programs where the initial software application is created by humans, but after which it can develop itself, learn through real-time experience, and adjust its strategies accordingly. The shift from discretionary to systematic hedge funds is fast taking place and is visible in the number of hedge funds being launched. In 2014, more than 40 percent of new hedge funds launched were systematic funds, i.e., they utilized computer systems to select investments, which was the highest ever till that time. AI-based hedge funds have also justified their existence by outperforming average industry returns consistently since 2008, with 2012 being the only exceptional year in which they under-performed.

Artificial intelligence is being used for natural language processing (NLP), sentiment analysis, big data analysis, and deep learning to explore investment opportunities. In addition to processing vast amounts of financial data to extract hidden trends, AI techniques enable the analysis of non-financial data like news articles, tweets, pictures, videos, etc., and recognize patterns though human-like inferences. Advanced techniques such as Bayesian networks and evolutionary computation are also being used to build machine learning models for hedge funds.

Artificially intelligent systems are being explored and developed by well-established hedge-funds like Bridgewater Associates, Two Sigma Investments, and Renaissance Technologies, as well as new AI-focused firms including Sentient, Rebellion Research, and Aidyia, with the total managed capital running into trillions of dollars.

Some examples include:
1) Two Sigma manages assets worth US$35 billion and uses advanced AI technologies to discover new investment opportunities. One of the applications it uses is NLP to analyze the Federal Reserve (FED) minutes of meeting in order to gain insights into focus areas of the FED
2) Hong Kong-based Aidiyia is another flag bearer which has developed a trading robot inspired by genetics. Aidiya started trading in US equities in 2015
3) San Francisco-based startup, Sentient Technologies, has also been trading using AI-based systems since last year
4) Numerai is another AI-based hedge fund which has been developed by a community of anonymous data scientists. It makes use of a monthly tournament to get AI based-models from various data scientists to predict the stock market movements. Numerai received a funding of US$1.5 million from Renaissance Technologies

Since its inception, AI has witnessed contrasting hype cycles. Currently, however, the industry seems primed for disruption and innovation and AI's evident broad-based applications and benefits make it a potential game-changer. But it will not be devoid of challenges - AI requires not just skilled human resources, but also technology infrastructure that can work on extremely low latencies. It will also need extremely supportive reconciliation, reporting, and various other operation support applications. In addition, the regulatory side of use of AI in hedge funds remains hazy. But as AI becomes more mainstream, more robust regulations will surface that will require financial institutions (FIs) to develop stringent controls and risk assessment frameworks.

Like any other disruptive technology, there would be some AI techniques that fail or lead to unexpected results. But overall, it could lead to some big systemic changes in the hedge fund industry.

Risk management - Evolving challenges and models

- By Mayur Bansal and Ashima

The post-crisis era has witnessed a slew of compliance regulations in the finance industry. New products, increased government scrutiny, and a strong focus on compliance, has brought greater risks and a larger set of rules and regulations. Financial institutions (FIs) must now review their compliance practices and the technology infrastructure that supports them, and pursue a broad range of compliance and risk initiatives. According to predictions, the global IT expenditure by FIs on risk IT services and systems is expected to be US$70 billion in 2016. This expenditure has been mainly driven by investments in the areas of governance and systems, and process integration. However, expenditure in additional areas, such as compliance, stress test reporting, data aggregation, enterprise crime and fraud, are also important for the growth of risk IT spending.

Industry participants and regulators are focusing on managing risks due to various costs associated with enterprise fraud, money laundering, market and credit positions. In addition to US$12.4 billion in monetary fines (till 2014, as per CEB TowerGroup), there are various hidden associated costs such as fraud remediation and ongoing monitoring costs, suspension of licenses, opportunity costs, reputation damages, and higher risk premium costs. FIs face various challenges when attempting to improve their risk management practices. One big challenge involves identifying, developing, and adapting to new technologies. The other is adopting proactive risk management and compliance methodologies. Availability of good quality data, ever increasing pressure to maintain / increase profitability, reducing margin of error, and growing complexity in impact analysis, are key factors that make it necessary for FIs to understand risk with a fresh perspective.

Risk management approaches adopted by FIs are being enabled by technology in many ways. Their current approaches utilize advances in the fields of big data, analytics, and storage technologies to make risk management more futuristic and predictive as compared to the traditional and backward-looking methodologies. The approaches are being fine-tuned to make them more integrated and holistic for real-time analysis and reporting, as per regulatory requirements. Risk management in financial services is continuously evolving with the expansion of the role of the Chief Risk Officer (CRO) or Chief Finance Officer (CFO) being introduced to take care of capital utilization, cash management, and regulatory compliance. CROs have now become the first line of defense with the responsibility of overall risk management. In addition, it is now crucial to invest and innovate in risk platforms. For instance, innovations such as blockchain technology and crypto-currencies have removed various intermediaries in the transaction processes. The innovations must focus predominantly on risk reduction with efficient and predictive technology.

Looking ahead, the business models of FIs will need to be risk-based with a focus on just-in-time risk management and analysis. The technology infrastructure will need to be reinvented to bring improved controls and compliance while decreasing operational costs. Further, holistic risk management frameworks will need more investments, as a fragmented approach will adversely affect the complexity, efficiency, and sustainability of FIs and their systems.

August 2, 2016

Bank(ing) on big data - Banks or fintechs?

- By Chetna Narayanan and Prasanna Sekar

Today Data is omnipresent and is one of the most talked about topics across the table - anywhere and everywhere. But if you think that only the availability of data makes sense to create better offerings or to generate better value in the financial services (FS) industry, then the answer is a big 'NO'! Why? Because only the proper channelization of data will create value-based offerings for customers.

Though banks have huge amounts of data available to them, they, inadvertently, do not utilize this data to the extent they are supposed to in today's highly competitive marketplace. Banks have in their possession a significant amount data, which can be represented as the voice of the customer (VoC) on different social media platforms to understand customer needs and behavior. Unfortunately, they are not technically competent to utilize this data to create a more engaging customer service

Banks own and enjoy the advantage of data volumes, which comes in all shapes and sizes: horizontal, vertical, structured, semi-structured, and unstructured. Despite banks and financial institutions being such significant custodians of financial data, they do not use data points innovatively to create value-based offerings to customers as they are still struggling to be technically competent. However, they are powerhouses in terms of data collection and this is where fintechs firms like PayPal or TransferWise - are playing an active role in building customer-centric products and taking the financial innovation concept to banks

Among fintechs, big data analytics is one of their favorite roundtable discussions as they are utilizing and putting data into action by creating value for traditional banks through increased efficiency and cost reduction.

Banks provides plethora of customer data but it's of no use if it cannot be retrieved, validated, disseminated, and effectively converted into actionable insights. Today, fintechs are helping banks to disseminate the customer information so they can use it effectively to do deep dive analysis of consumer's financial behavior, enabling the delivery of much more personalized and comprehensive range of services.

For example, Barclays uses big data and Hadoop to come up with services called the Local Insights and Smart Spend. This is their attempt to stay technologically competent to understand and engage with their clients.

Fintech firm Trulioo has created a first-of-its-kind database for rural African population, which will help in searching for personal data across regions, which was not even previously considered by big banks.

Fintechs 1010Data and Xignite have created new, cloud-based data-sharing models, which will be useful for countries like Luxembourg, which is stringent on privacy regulations.

As fintechs continue to evolve their big data and analytical offerings, banks will establish and define each customer's experience according to individual's requirements, much before s/he can request a specific service. Though banks have started to adopt big data more and more over the last few years, there are still a lot of areas in which it could be leveraged and hopefully we will see more innovation in the use of big data techniques by banks.

Financial innovation is no longer a game of isolation. The owners of data (banks) and the kings of innovation (fintechs) are collaborating with each other and they need to as the former possesses the scale of data and the latter has the ability to convert that data into real-time actions - to take the FS industry to greater heights of customer innovation and deliver a truly engaging experience.

July 25, 2016

Chat bots: So banking can be as easy as chatting

- By Kiran Kalmadi and Durga Prasad Balmuri

Imagine you are chatting with your friend Vikki about a new film, over a messaging app, when you suddenly realize that you need to transfer money to her and book yourself an Uber cab as you are heading out for dinner. In a regular scenario, you would log into your mobile banking app, authenticate yourself, enter the required payee details, and submit a request to initiate the transfer. You would then open the Uber app to book your ride. What would it be like though, if you could do all this without leaving the messenger? As it happens, that is now perfectly possible.

Enter 'chat bots'*. With a chat bot integrated into your messenger, all you need to do is type a simple message like "Send $100 to Vikki," to transfer money, and "Get me an Uber," to book a cab -- all right within the messenger.

Since the beginning of 2016, the focus on chat bots has increased significantly. The likes of Facebook, Google, Microsoft, and Apple are all investing in bots and looking at ways to integrate them into mainstream applications. Interestingly, the 'bot' jargon gained popularity when in April 2016, Facebook announced that it was opening up its 'Messenger' platform to third-party chat bots. This meant that people would be able to interact with bots in Messenger the way they talk to their friends -- all thanks to artificial intelligence (AI), natural language processing (NLP), and some human help. In fact, many banks have recently started showing a keen interest in chat bots.

Today, banks from across the globe are exploring chat bots as a means to answer FAQs, educate their consumers, send them personalized offers, and help them with everyday banking activities like making enquiries about balances, transferring money, paying bills, etc. While the Royal Bank of Scotland (RBS) already has 'Luvo,' an AI bot meant to answer customer queries, Bank of America and American Express (AmEx) are embracing Facebook Messenger's chat bots. Bank of America's bots will enable its customers to receive real-time alerts and important communication. The AmEx bot, meanwhile, is working on innovative ways of interacting with customers. Once a customer opts for AmEx bots, they will start receiving real-time notifications about their purchases, alongside personalized alerts like their card's benefits and purchase-related services. Just this month, Russia-based 'Tochka Bank,' of the Otkritie Financial Group, launched a Facebook Messenger bot for a whole range of financial services. The bot will help the bank's customers check their account balance, contact customer support, make payments, and help them find nearby ATMs via GPS.

Considering the variety of possible offerings, chat bots can not only be a convenient way for customers to interact with their banks, but can also help banks increase engagement levels and thereby improve loyalty. However, challenges like authentication issues and user errors mean that banks may initially use Messenger bots where authentication and funds are not involved -- like search, general enquiries, and education -- and with time, slowly graduate to live chats and alerts, and then finally include transactions that require authentication
 
This is just the beginning and the area is expected to see a lot more traction over the coming months. Until then, keep watching this space for further updates. Amidst all this, I forgot to ask Vikki - did you receive the $100 I transferred?

*Webopedia defines 'Chat bot' as being short for 'chat robot', a computer program that simulates human conversation, or chat, through artificial intelligence.

July 11, 2016

Smart machines: Will they disrupt the banking industry?

Decades ago, self-driving cars were nothing more than an unrealistic dream. But in today's tech-savvy world, this dream is now a reality ― with the concept of smart machines. Once considered in the same vein as our childhood super heroes, smart machines are now all set to rule our industries.

Smart machines are basically intellectual devices that use cutting-edge technology to displace human chores. These machines can make use of loads of facts and figures from various sources to offer new kinds of information and can analyze huge amounts of data and information to reach conclusions that human beings can't even begin to imagine!

Examples of smart machines include self-driving cars and cognitive computing systems that can process problems, make choices, and provide solutions without the need for human involvement. In fact, smart machines are poised to be the next digital disruptors across all sectors! They will revolutionize the way businesses are run and can be especially useful within the banking industry.

Smart machines can offer huge latent benefits to early adopters in the financial services sector, including better customer experience, higher productivity, and larger profits. Banks are now deploying smart machines across an array of jobs. In fact, many banks will replace the jobs humans used to do, while others will introduce services that were not possible earlier. Thus, it is crucial for IT decision-makers within banks to prioritize prospective investments into smart machines.

Applications of smart machine technology in the banking industry include smart vision systems, virtual customer assistants, smart advisors, smart security, virtual personal assistants, as well as smart infrastructure.

An animated avatar of a virtual customer assistant, which is smart enough to offer intelligent help to customers while creating a fun experience ― such as the flapping and drumming on the screen of a tablet or a smartphone ― is an example of the kind of smart machines that banks will look to invest in the near future. Similarly, the demand for smart vision systems is likely to increase dramatically for the purposes of identifying thefts, authenticating using laser ray technology, measuring consumer attention spans to predict customer actions, etc.

Smart infrastructure is another innovative area in which banks will soon be investing in. Smart detection technologies implanted in building infrastructures can help bank executives predict problems, make well-versed decisions for asset maintenance, and even use green technologies.

So by when will we be able to witness the impact of smart machines in the banking and financial industries? Well, it is expected that most banks will invest in smart machines and their supporting technologies over the next few years. However, an impact of this will be the removal of millions of banking jobs, specifically in the US and UK. However, to counter this and to sustain superiority over machines, bankers should look to move more complex roles like expert thinking, breakthrough ideation, and complex communications to us humans while leaving the more mundane banking chores to the smart machines.

June 13, 2016

Exit for Britain, uncertainty for banks

-by Kuljit Singh and Siddhartha Chanda

We are currently living at a time when global uncertainty has almost become a norm. From the financial crisis in the US to debt problems in Europe, geopolitical tensions in the Middle East, and the migration crisis in Central Europe, we have witnessed it all. The latest to create havoc in the financial market is Britain's referendum on leaving the EU, also known as "Brexit". The referendum, due on Jun 23 2016, has brought along with it uncertainty again - the consequences of Britain voting to leave EU.

Here's a snapshot of UK's presence in the European financial market - in cross border lending, the UK holds around 17% of international market share in comparison to 9% by France and Germany. The UK dominates in hedge fund assets which amounts to around 18% of the market share compared to 1% by France. In addition, it is now the biggest centre in the world for trading the euro.

In terms of numbers, a latest study suggests Brexit has the potential to disrupt 100,000 jobs by 2020 in the financial services industry. Losses are expected to occur to the tune of more than GBP 17 billion and it could reduce the sector's contribution to the national economy by up to GBP 12 billion. Some believe there would be 60% changes in the existing law, which means only one thing - trouble for the financial services sector in maintaining compliance and a possible gold rush for lawyers. Due to these changes in the rules and regulations both in EU and the UK, "RegTech" will be one of the most crucial area of focus for banks. They would need to come up with a comprehensive strategy to ward off the challenges coming from this revised regulatory overload.

Coming to capital markets, the UK is the lynchpin of whole structure of interconnected economy to an extent that more than 3/4th of all capital market business in EU27 is conducted out of the UK. Unsettling this structure could have a domino effect on all the players in the group.
One of the biggest benefits of being in the EU for financial institutions is the benefit of passporting. In simple terms, passporting is the right given to banks in a member state to carry on cross border business and sell services across Europe without obtaining a license. And this is one of reason why many third country banks have chosen to base themselves in London - to have access to the EU markets. Banks are able to access and operate across the EU under CRS or prudential passport which can be termed as a single banking license for the entire region. This leads to a reduction in the complexity and cost for their cross-border operations. Investment banks also work on a similar model under the Mifid passports and can service their clients across the EU. In the event of Britain exiting EU, banks and investment banks may have to find alternatives such as separately capitalized subsidiaries and separate broker dealers. This might challenge London's role as the venue of choice for global firms to conduct their European business.

At this juncture, nobody is sure about the outcome of the referendum. The economist's Brexit poll tracker suggests a marginal lead for the "remain" camp while some other individual polls show a major¬ity favoring a "leave" vote. Nevertheless, what it actually boils down to is that the voters will be choosing between a status quo and a complex process of negotiation and uncertainty.

May 26, 2016

Social trading is redefining trading

Thanks to bionic advisors, my investments (better read) as long-term wealth is taken care off! But who doesn't want some quick profits, higher returns? By now, you would have guessed that I am talking about stocks, stock trading, to be precise. Here's my stock trading story. Before putting my money in stocks, I decided to seek guidance from my colleagues who have been trading for decades now. Unfortunately, they weren't interested in sharing their strategies with a debutant trader. So I began researching on the Internet to understand the stock markets better and that's how I stumbled on an interesting new concept - social trading.

In a nutshell, social trading is a mechanism of bringing together traders across the globe into one big network and providing traders the option to leverage trading techniques and strategies of other traders. Unlike the old school of thought on trading where trading strategies were closely guarded secrets, this new trading concept allows traders to follow or even blindly copy trading strategies of top investors.
Social trading is being considered as the next big phenomenon in the capital market. The social trading platform is a vast ocean of information, available for free for any trader. The perpetual data flow enables traders to make profit out of trade even if they don't possess any erstwhile information or experience on trading. Thus, it seems to be the best choice for traders especially, novice traders. And it helps the top investors get some extra pounds by sharing their trading skills.

On realizing that social trading is the best option for me, I started looking for a viable social trading system. Interestingly, I found that fintechs are extremely active in the social trading space. Though investment banking had been sort of opaque to fintechs, the rising wave of social trading is seeing lot of traction from fintech firms such as ZuluTrader, eToro and StockTwits. Currently, ZuluTrader is the largest global social trading network with the highest number of traders and investors. StockTwits, on the other hand, is a social communication platform that uses tweets for trading while eToro aims to help novice investors. Other well- known players in this space are Ayondo, Tradeo, SignalTrader, and more.

I glanced through a few of these leading platforms and found that the instruments traded on these platforms are quite widespread, covering stocks, forex currency pairs, gold, silver, commodities, indices, oil, etc. These platforms are quite user friendly and most of them include a live feeds feature, giving higher visibility on the trading operations of all traders. After viewing the feeds, based on my choice and requirement, I could "follow" the top traders / investors. Then if convinced, I could blindly "copy" the trades of those leading investors - essentially, allocate a portion of my funds to the selected investor / trader and their trades gets automatically copied, making my life easier and richer. Interestingly, some of the solutions even enable the traders to get in touch with the topline investors directly to clarify their concerns.

Social networking worked and is thriving, will social trading see a future? Actually, a lot of action is already happening in the social trading arena - many firms, especially, fintechs are entering this dynamic and active space. Firms like Ayondo are spreading its wings to growing markets like Asia, local regulators across the globe are showing profound interest. All this indicates that social trading is bound to stay and would definitely be the resort for the tech-savvy millennial who prefers fast and smoother trading experience. However, the sustainability of the concept depends on how well the knowledge of traders can be applied or tweaked to the likes of the other traders.

May 23, 2016

Analytics is the oxygen that energizes new banks to scale new heights of modernization

Large, global banks process billions of transactions across service offerings to a plethora of customers across demographics, daily. In order to sustain effective operations, they must adopt cutting-edge analytics that churn the petabytes of rich information into valuable insights.

As of today, most global banks are processing these petabytes of transactional data through legacy and modern databases that get downgraded through years of mergers and acquisitions. Therefore, migrating complete legacy and distributed data towards a robust storage solution that addresses cur-rent challenges and future requirements, marks the first step towards modernization.

Having said that, banks also have to make sense of two data formats -- unstructured and un-leveraged format from legacy databases, and structured data from new tools in big data and analytics. Towards that, they must implement solutions that center on R, Python, SAS, or NoSQL driven analytics. Not only do these solutions integrate structured and unstructured information, but also process it like a fast moving Pac-Man! In fact, they produce unbelievable outcomes, occasionally influencing strategic outcomes and are mostly open source. At the same time, Blockchain technology is a new kid on the block! To maximize value from the opportunities that Blockchain presents, banks require top-class analytical and data processing capabilities.

Therefore, business analytics is an invaluable capability for organizations. It augments competitiveness of service offerings, market growth, and relevance from the current perspective. Simultaneously, cog-nitive / predictive analytics, which has been neglected for quite some time, is equally important to en-sure anti-money laundering (AML) / fraud detection. For a considerable amount of time, banks have overlooked the hazards of incomplete and missing information across Know Your Customer (KYC), Know Your Employee (KYE), Customer IDentification (CID), Customer Due Diligence (CDD), and En-hanced Due Diligence (EDD) processes, while driving competitiveness. Today, such negligence can prove costly and I can extend the point in discussion to Cash Management services which Banks ex-tend as a value-add for a small fee. In the current scenario, cash management teams must focus atten-tion towards cognitive / predictive analytics for AML / fraud detection. This is because these teams directly handle cash coming from external sources, which could be honest or obscure with a dark un-derbelly. Consequently, services that cater to cash collection, dropbox, vault, sweeps, zero balance accounts, and cash concentration are abused. This is because some of these overlap with the realms of private banking, which hide the true beneficiary behind the wraps of secret arrangements and agree-ments.

In my next blog, I will discuss about how applying R, Python, or SAS on the available 'structured' infor-mation combined with available 'unstructured or free' information will come handy towards harness-ing the power of fast and efficient analytics, using underlying legacy and modern silos.

April 21, 2016

Bionic Advisors: A Human & Technology Mash Up!

It's April - a new financial year; a time when terms like 'higher taxable income,' 'investments,' and 'Sections 80C, 80D, 80E,' keep buzzing in my head. Now, although tax planning is certainly on my cards, the 'when, where, and how' of investing is still unclear, thus driving me to seek financial advice. To avoid the hassles associated with traditional financial advisors, I initially thought of using automated advisors that are popularly known as robo-advisors; but, then I realized that these models will not provide personalized advice in important matters. That's when I found the perfect answer to my investment woes - 'bionic-advisors.' Yes, you read it right; not 'robo,' but 'bionic'.

'Bionic-advisors' make use of technology to enhance client relationships. They use a specialized, automated advice software to create reports that are used as a base for all client interactions, which happen either via web portals or mobile devices. Additionally, these advisors use automation in various areas, such as creating customized client reports, paperless onboarding processes, and rationalizing KYC processes. As a result, bionic-advisors save time and increase efficiency, allowing them to focus better on client interactions. Although bionic-advisors are very similar to robo-advisors -- especially in terms of cost effectiveness and transparency -- they still score over robo-advisors, thanks to the human element in the bionic model.

Individuals like me, who have certain financial goals, but still require personalized advice; will prefer bionic-advisors. They provide automated portfolios and reports that investors can use at any point of time and, most importantly, they give us access to  interact with financial advisors when we feel the need for them. Interestingly, more than low-net-worth individuals, it is the high-net-worth individuals (HNIs) who seem to benefit the most from bionic-advisors. Most HNIs want their preferences to be incorporated into their huge portfolios, and demand customized financial advice for their complex investments. Consequently, they prefer a bionic mix over other advisory models.

Off late, 'financial advice' has been going beyond the realm of investing and instead, has taken the shape of financial planning. The mere presence of automated advisors will not completely quench the evolving financial needs of today's investors - a fact that only strengthens the case for bionic-advisors. Firms like AdviseSure are already making headlines. Launched in 2015 in India, AdviceSure is a bionic-advisor, providing advisory services across multiple product categories, such as mutual funds, capital market, shares and stocks, systematic investment plans (SIPs), tax-saving schemes, national pension system (NPS), etc. And now, they are in talks with venture capitalists to raise funds worth US$5-8 million, which will be used for marketing their service and improving their technology in the future.

The bionic-advisory model is a lot like hot chocolate fudge sundae. Everybody loves vanilla ice cream, just as they love chocolate sauce; but it's when the two come together that the final product becomes truly delightful. The bionic-advisory model's USP is that it integrates automation with human interaction, thus ensuring the best advisory solutions for investors. In the end, it is the measure of delight that bionic-advisors will offer their investors, which will decide the model's sustainability in the long run.

April 13, 2016

Don't ignore SME Lending: Alternative Lenders are here

-by Kiran Kalmadi and Durga Prasad Balmuri

The next focus in the David Fintechs Vs Goliath Banks series is on SME lending. In 2014, 28 million small and medium-sized enterprises (SMEs) in America contributed to over 50% of the U.S. non-farm GDP. Yet, most of them find it very challenging to secure the capital required to either run their daily operations, or invest in business expansion; as small business lending is largely neglected in many countries.

At the same time, many banks have also seen a dip in their share of lending to small businesses over the past few years. For example, loans issued by the top, 10, U.S. banks dipped to US$44.7 billion in 2014 vis-à-vis US$72.5 billion in 2006. This could be attributed to the fact that banks find it unviable to cater to this segment due to a number of factors such as the small loan size, strict regulations, heavy paperwork, etc. These factors make lending expensive, considering the relatively low returns that they generate. In addition, banks also expect borrowers to perform well on three parameters - credit scores, collaterals, and cash flows - before extending loans; and not all SMEs can meet these criteria.

Luckily, every cloud has a silver lining and these challenges have catalyzed the growth of a new type of online non-bank lender or tech-based alternative lender. They disburse loans via online platforms using advanced, underwriting algorithms and new credit appraisal methodologies. Unlike traditional banks, these lenders refuse to depend on just the three parameters, and analyze additional parameters like bank transaction history, tax filings, credit card history, invoice volumes, etc., before disbursing loans. The insights gained from these additional data points greatly improve the chances of granting loans to SMEs. This way, platform-based companies can quickly underwrite loans of small-ticket sizes that banks find trouble servicing.

What's more, by leveraging technology, online platforms, and innovative risk assessment methodologies; most alternative lenders take only a few minutes to assess if an applicant qualifies for a loan. In other words, approval mostly happens on the same day and is ideal for most businesses that need regular financing and cannot wait for weeks every time they need funds.

Such customer-centric processes make alternative lenders like OnDeck, Kabbage, Fundation, Funding Circle, and many others; rockstars in the SME lending ecosystem. Stars who take on a lot of risks including unclear regulations and a higher probability of loans defaults. Under these circumstances, alternate lenders are reducing risks by charging higher interest rates that could reduce in the near future once they become more mainstream, secure funds at cheaper rates by partnering with banks, and establish firm roots in the lending space.

The bottom line remains that alternative lenders may turn out to be more favorable to SMEs than traditional banks. By offering a life-line to small business owners, they might soon start gnawing at the balance sheets of banks; implying that banks must come out of their comfort zone and act upon their inherent challenges faster than ever before.