3/9/16: Fintech, Banking and Dinosaurs with Wings

Here is an interesting study from McKinsey on fintech role in facilitating banking sector adjustments to technological evolution and changes in consumer demand for banking services:
http://www.mckinsey.com/business-functions/risk/our-insights/the-value-in-digitally-transforming-credit-risk-management?cid=other-eml-alt-mip-mck-oth-1608

The key here is that fintech is viewed by McKinsey as a core driver for changes in risk management. And the banks responses to fintech challenge are telling. Per McKinsey: “More recently, banks have begun to capture efficiency gains in the SME and commercial-banking segments by digitizing key steps of credit processes, such as the automation of credit decision engines.”

The potential for rewards from innovation  is substantial: “The automation of credit processes and the digitization of the key steps in the credit value chain can yield cost savings of up to 50 percent. The benefits of digitizing credit risk go well beyond even these improvements. Digitization can also protect bank revenue, potentially reducing leakage by 5 to 10 percent.”

McKinsey reference one example of improved efficiencies: “…by putting in place real-time credit decision making in the front line, banks reduce the risk of losing creditworthy clients to competitors as a result of slow approval processes.”

Blockchain technology offers several pathways to delivering significant gains for banks in the area of risk management:

• It is real-time transactions tracking mechanism which can be integrated into live systems of data analytics to reduce lags and costs in risk management;
• It is also the most secure form of data transmission to-date;
• It offers greater ability to automate individual loans portfolios on the basis of each client (irrespective of the client size); and 
• It provides potentially seamless integration of various sub-segments of lending portfolios, including loans originated in unsecured peer-to-peer lending venues and loans originated by the banks.

Note the impact matrix above.

Blockchain solutions, such as for example AID:Tech platform for payments facilitation, can offer tangible benefits across all three pillars of digital credit risk management process for a bank:

• Meeting customer demand for real-time decisions? Check. Self-service demand? Check. Integration with third parties’ platforms? Check. Dynamic risk-adjusted pricing and limits? Check
• Reduced cost of risk mitigation? Yes, especially in line with real-time analytics engines and monitoring efficiency
• Reduced operational costs? The entire reason for blockchain is lower transactions costs

What the above matrix is missing is the bullet point of radical innovation, such as, for example, offering not just better solutions, but cardinally new solutions. Example of this: predictive or forecast-based financing (see my earlier post on this http://trueeconomics.blogspot.com/2016/09/2916-forecast-based-financing-and.html).

A recent McKinsey report (http://www.mckinsey.com/industries/financial-services/our-insights/blockchain-in-insurance-opportunity-or-threat) attempted to map the same path for insurance industry, but utterly failed in respect of seeing the insurance model evolution forward beyond traditional insurance structuring (again, for example, FBF is not even mentioned in the report, nor does the report devote any attention to the blockchain capacity to facilitate predictive analytics-based insurance models). Tellingly, the same points are again missed in this month’s McKinsey report on digital innovation in insurance sector: http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/making-digital-strategy-a-reality-in-insurance.

This might be due to the fact that McKinsey database is skewed to just 350 larger (by now legacy) blockchain platforms with little anchoring to current and future innovators in the space. In a world where technology evolves with the speed of blockchain disruption, one can’t be faulted for falling behind the curve by simply referencing already established offers.

Which brings us to the point of what really should we expect from fintech innovation taken beyond d simply tinkering on the margins of big legacy providers?

As those of you who follow my work know, I recently wrote about fintech disruption in the banking sector for the International Banker (see http://trueeconomics.blogspot.com/2016/06/13616-twin-tech-challenge-to.html). The role of fintech in providing back-office solutions in banking services is something that is undoubtedly worth exploring. However, it is also a dimension of innovation where banks are well-positioned to accept and absorb change. The real challenge lies within the areas of core financial services competition presented (for now only marginally) by the fintech. Once, however, the marginal innovation gains speed and breadth, traditional banking models will be severely stretched and the opening for fintech challengers in the sector will expand dramatically. The reason for this is simple: you can’t successfully transform a centuries-old business model to accommodate revolutionary change. You might bolt onto it few blows and whistles of new processes and new solutions. But that is hardly a herald of innovation.

At some point in evolution, dinosaurs with wings die out, and birds fly.

2/9/16: Forecast-based Financing and Blockchain Solutions

As the readers of this blog know, AID:Tech (https://aid.technology/) is a new venture I am involved with that uses blockchain platform for provision of key payments facilitation services for people in need of emergency and continued assistance (refugees, international aid recipients, disaster relief aid and general social supports payments). As a part of the market analysis and strategy, we have encountered an interesting, rapidly evolving services segment relating to disaster relief: the concept of Forecast-based Financing (FBF) worth highlighting here.

Under FBF, aid providers release humanitarian aid-related funding ahead of the adverse event taking place, based on forecast information that aids in predicting the severity, timing and impact distribution of the disaster (natural or man-made). This approach to aid delivery aims to:

• Reduce key risks (e.g. assuring that delivery is timed in line with the adverse shock, focused on key geographic and demographic audiences, uses pre-disaster - and thus more efficient - supply chain networks, etc), 
• Enhance preparedness and response (by increasing quality of aid targeting and allowing to concentrate resources in the areas where they are needed most and ahead of the actual disaster impact), and 
• Make disaster risk management overall more effective by assuring that aid resources are present at the time of the disaster and after the disaster impact, thus reducing losses and delays in delivery of aid that may arise as the result of the disaster (e.g. destruction of roads and disruptions in power supplies, etc).

In general, FBF framework is open to several questions and objections, all requiring addressing.

How does FBF work? 

A humanitarian aid agency and stakeholders (e.g. meteorological services and communities at risk) jointly create a contingency plan, outlining key actions to be taken ahead of the probabilistically likely disaster or shock. They also set out specific metrics that define the trigger for aid pre-delivery, based on a model risk forecast reaching a specific threshold of probability. Linked to severity of forecast shock, specific budgets are set aside for activation. Once the risk probability threshold is breached, aid is delivered to the location of possible disaster, using pre-disaster supply chain management structures before these get disrupted by the event.


Forecast errors: are these really costly?

Probabilistic forecasts are never 100% accurate, which means that in some instances, aid will be delivered to the communities where the adverse event (a shock) might end up not materialising, despite probability models generating high likelihood of such an event. In a way, this is the risk of aid agencies providing disaster relief “in vain” or “wasting” scarce resources. It is worth noting that probabilistic errors of “wastage” can be significantly over-estimated, as some disasters can be relatively well forecast in advance (http://www.nat-hazards-earth-syst-sci.net/15/895/2015/nhess-15-895-2015.pdf). Quality of forecasting will, of course, co-determine losses in the system.

To achieve system-wide efficiency and secure gains from implementing an FBF programme, one has to be able to counter-balance the benefits of early response,including those arising from more efficiency in accessing supply chains pre-disaster and reducing the cost of disaster, against the likelihood of a loss due to probabilistic basis for the action. This can be done via two channels:

1. Assuring that during planning, the cost of acting pre-emptively, including the cost of probabilistic ‘waste’, is factored into planning for which forms of aid should be pre-delivered and on what scale; and
2. Assuring that aid supply chain and forecasting models are optimised to delver highest efficiencies possible.

Over time, development of FBF will also require changes in supply chain management to mitigate losses due to “wastage”. For example, putting more emphasis on local (or proximate) sources for supply of critical aid can reduce “wastage” by lowering cost of deliveries and by closely anchoring pre-disaster deliveries to existent markets for goods and services (so at least some pre-delivered aid can be returned into local markets in the case if probabilistically likely disaster does not materialise).

In other words, aid agencies and potentially impacted communities need to have access to timely and accurate information on which resources are needed in responding to a specific disaster, on what scale and, crucially, which resources are already available in the supply chain and in the local or proximate markets. The key element to this is ability to track in real time supply chains of goods and services accessible at differential cost to specific communities in cases of specific disaster events. The agreed (in advance) standard operating procedures (SOPs) that are set between the aid providers and the recipient communities must be both realistic (reflective of measures necessary in the case of specific disaster) and effective (reflective of the balance of cost-benefit).

Put differently, the process of FBF is the process of, first and foremost, planning and data relating to supply chain management.


Are there any tangible experiences with FBF?

One early example of FBF implementation is the case of the Red Cross Red Crescent Movement that has field-tested an FBF programme Uganda and Togo. This project bridged financial and technical support from the German government and Red Cross, and used technical support from the Climate Centre.

Another case is of FoodSECuRE initiative by the World Food Programme that is currently in planning stages. In this programme, private sector partners (aviation services providers, insurance companies etc) are engaged in FBF planning for alleviation of potential flooding due to El Niño effects in Peru (http://www.climatecentre.org/downloads/files/FbF%20Brochure4.pdf and http://www.climatecentre.org/programmes-engagement/forecast-based-financing). Both of these experiences show also the importance of setting aside sufficient response funds for FBF delivery.

Further afield, FBF pilots are being run or planned by the WFP and other organisations in Bangladesh, the Dominican Republic, Haiti, Mozambique, Nepal and the Philippines.

Note: the above cases were provided by the UNFDP research.


Overall, FBF is becoming one of the cornerstones of the global disaster aid delivery programmes and was endorsed by UN OCHA and the IFRC. FBF was also included in the International Federation’s special report ahead of the World Humanitarian Summit in Istanbul. The report included a pledge to facilitate a doubling of FbF within the Movement by 2018.

However, despite the aid agencies enthusiasm, the key problem relating to FBF remains largely unaddressed: currently, with some 20 percent of disaster aid being lost due to insufficient supply chain management, fraud and theft, delivering properly structured FBF requires exponentially greater exposure to data collection and analysis, as well as to strengthening of real-time supply chain visibility systems.

As AID:Tech example shows, these objectives can be supported via private and semi-private blockchain solutions.