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FINTECH AND BANKS: CONJOINT TWINS AT THE ANALYTICS HIP - PART 2

2016-06-10/ Saurav Parida

Part II – Friends or Foes

The second and final part of the blog lays out the various trends that one foresees in the FinTech space that Banks will have to adopt or adapt to in order to stay in the game. However, first they have to be mindful of the inherent failings that would need a huge overhaul within traditional setups to take on the challenge posed by the new agents of change. Eventually the Banks will have to resolve the dilemma of whether they should consider aligning or going-it-alone unless they want to stay rigid and wither away. In 1997, Bill Gates predicted the eventual demise of banks: “We need banking but we don’t need banks”.

Trends in FinTech courtesy Analytics

Consumer Banking

There is increasing development and integration of software-as-a-service (SaaS) into banking operations which foster moving away from physical channels towards digital/mobile delivery in order to improve customer experience. SaaS helps streamline operational capabilities and assists in offering customers a wider array of options, unlimited global access, round the clock service, which can be constantly upgraded. Through SaaS, businesses are now organising around the customer rather than product or channel. For example, Bancorp provides middle-ware platform (licenses, processing and integration) for a fast and cheap launch of FinTech start-ups.

Payments and Fund Transfer

Mobile smartphone adoption has led to consumer expecting immediacy, convenience and security of payments. Also, consumers have begun to expect a consistent omni-channel experience making digital wallets key to stream-lining user experience and reduce customer friction. There is greater demand for solutions that leverage biometrics for fast robust authentification coupled with obfuscation technologies such as tokenisation. Speed, security and digitisation will be growing trends for the payments ecosystem. Toastme allows unbanked expatriates to send money back home instead of queuing at Western Union.

Asset/Wealth Management

There is increased sophistication of data analytics to better identify and quantify risk. Wealth managers are increasingly using analytics to form a more holistic view of customers to better anticipate and satisfy their needs. Increasingly low cost and affordable automated advisory capabilities are becoming a pre-requisite for the multi-trillion dollar wealth management industry to tap into the virgin territory of small retail investors. For example Yodlee, a cloud-based platform, looks at past earning and spending behaviour to offers range of fiscal advice unique to the individual.

Technology

According to predictions by Gartner1, by 2018 20% of all business content will be authored by machines, 45% of fastest-growing companies will have less employees than smart machines and 3million+ workers will be supervised by robo-bosses. Key technologies to watch out for in the analytics space are

(i) Hadoop: mainstreaming of Hadoop as commercial vendors aggressively plug gaps for production (data security, governance etc). Extract, Hadoop and load (EHL) will gain popularity over extract transmit and load (ETL). Hybrid architectures where traditional enterprise data warehouse vendors have created connectors with Hadoop will continue to gain currency.

(ii) Apache Spark: already the most popular open source project in the Hadoop ecosystem will see increasing adoption over Hadoop’s original MapReduce for real-time analytics including stream processing and machine learning.

(iii) Next generation EDW will focus on real-time intelligence. In-memory databases like memsql are seeing increased adoption.

(iv) Cloud-based analytics – will gain wider adoption, AWS and Azure will continue to lead and mature their PaaS (platform as a service) offerings while new DBaaS (database as a service) offerings like Snowflake and IBM Cloudant will gain acceptance.

(v) Machine Learning - Predictive analytics powered by machine learning has taken off and will continue to grow. However quality data will be a challenge as data silos need to be bridged.

(vi) Deep Learning/AI – There will be increasing venture capital funding for AI start-ups. Deep learning will gain adoption in image recognition and language understanding.

(vii) IoT – BY 2020, Gartner2 has predicted that 25bn IoT devices would have come online whereas Cisco3 has forecasted a more aggressive number of 50bn devices (linking tires, roads, cars, supermarket shelves and even cattle!). Cloud vendors like AWS, Azure have come up with targeted cloud offerings for IoT.

Blockchain

It is a combination of a number of mathematical, cryptographic and economic principles without the need for a third party validator or reconciler into a decentralised distributed ledger system. Just as ERP (Enterprise Resource Planning) software allows optimisation of business processes within a corporation blockchain allows entire industries to share data with different economic objectives. Currently it is a low priority for most FinTechs/Banks due to low level of familiarity but there is increasing inquisitiveness. With time, adoption of blockchain will result in highly efficient business platforms due to huge cost-savings in back-office, increased transparency that is positive from an audit & regulatory standpoint & automate & speed up manual and costly processes. It is estimated4 that blockchain has the potential to reduce settlement time, from 3-days to 10-minutes, and 99% of settlement risk exposure in capital markets.

Key Challenges for Banks

The incumbent Banks are becoming (i) displaced by superior customer experience and price offerings (ii) diminished through revenues in a difficult customer retention environment and (iii) dis-intermediated by new technologies, due to underlying structural weakness and some of them are as follows:

Customer vs Product Centricity

Banks continue to run on a business model where the wealthiest command a customer-centric relationship while everyone else gets a product-centric approach which runs completely contrary to the current trend of customer specificity. Banks are at a huge disadvantage in an environment where there are increasing demands and expectations by customers, with more and more Millenials coming onstream, for a personalised customer experience and no longer satisfied with a one size fits all approach.

Culture

By design big sized banks tend to be burdened by their size and global reach into having a conservative and bureaucratic culture that is averse to change and taking risk. Hence they are usually slow at the uptake when it comes to new opportunities that arrive at the scene. Also, Banks are hesitant to explore new business models that could cannibalise or compete with existing ones. Given the pace of development in analytics, Banks are handicapped to stay abreast with changing times ab initio and require a long transitionary period to internalise the nimble and dynamic culture of FinTechs.

Talent

Banks have to compete with all industries for the same type of skills and technology savvy Millenials (Generation X and Y) don’t view banking as the most attractive career. Also, given the constraints in freedom and lack of innovation within Banks, it makes them less appealing to up and coming talent. Also existing work force are less in tune with latest developments in analytics and tend to guard their own turf which makes adopting change even more difficult. Also, staff is not trained to easily access and learn from large swathes of internal data and garner important insights for business.

Resources

As the saying goes ‘too many cooks spoil the broth’. Similarly, Banks are burdened with multiple priorities: reducing costs in a low interest environment, the economy not doing too well, regulatory pressures of compliance, constant monitoring of risk across diverse business areas have led to not enough resources being allocated for building strong analytics platform. Also, with a very high cost of compliance it’s harder to deliver change at the rate of agile FinTechs.

Non-urgency

Despite increasingly ceding space to FinTechs, recent study5 shows that only 47% of the largest regional/national banks (asset size of $10bn+) in the US rank improving data analytics as amongst the top 3 priorities and is even lower, at 8%, for community banks and credit unions.

Collaboration or Competition

There are 2 types of FinTechs, the first ones to arrive on the scene were challengers to incumbent Banks. The second wave of FinTechs were collaborative that wanted to enter into a mutually beneficial arrangement with Banks and enhance the position of incumbents. Initially it was easy for competition to catch the incumbents unaware and first target less profitable segments and then attack core banking. But the Banks are beginning to fight back and so there is a greater willingness by FinTech to turn collaborators as they lack the financial muscle and need access to critical consumer data in possession of Banks. According to Accenture report6, collaborative FinTechs in 2015 represented 44% of total as opposed to 29% in 2014 and this trend is expected to continue. Also with time FinTechs will face the Spiderman dilemma: “With great power comes great responsibility”. Similarly, with increased scale and global reach FinTechs will become too critical a risk to be left out of the clutches of regulators. Hence this is another reason to closely partner or merge with Banks to benefit from their rigorous experience of complying with torturous regulations and a safety net that protects them from any adverse financial impacts incase of careless breaches.

Also, there is reciprocity by the Banks as they have begun to ride the FinTech wave with 3 strategies that are mostly based on the principle of “If you can’t beat them join them”: Option 1 - invest in FinTech start-ups , Option2 – Create partnerships/joint ventures with FinTech, Option 3 - create internal R&D labs from scratch.

Out of the 3 strategies the third option is the most difficult for Banks to adopt since they lack the basic culture of FinTech and it is extremely difficult to internalise an attitude that is alien to banking and rid themselves of legacy processes, technology and even people. If Banks were to go it alone the best bet is to create a standalone organisation (Beta Bank), quarantined from the older business model, with a separate leadership, culture, technology and talent pool where incubation of the latest innovations is possible with fewer constraints.

The first 2 options create a symbiotic relationship between the two where FinTechs have the better machines (advanced analytics, digital technology) whereas Banks have the fuel (consumer and transaction data and past patterns) and much needed financial muscle which when working together synergistically will arrive at superior solutions/platforms. Hence Analytics is the glue that binds them together in an age where customer experience has taken centre stage and customer expectations have risen dramatically as the world is awash and drowning in data courtesy the digital era. Banks and FinTech will have to join forces to stay ahead of the game by utilising cutting-edge algorithms and analytical tools on ever expanding data.

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