Category Archives: Fintech

2/2/21: The Disaster of Investing via Smartphones?

Some stuff I've been reading that (sometimes) falls into current newsflow: 

Kalda, Ankit and Loos, Benjamin and Previtero, Alessandro and Hackethal, Andreas paper, titled "Smart(Phone) Investing? A within Investor-Time Analysis of New Technologies and Trading Behavior"from January 2021 (NBER Working Paper No. w28363, https://ssrn.com/abstract=3772602) :

The authors tackle an interesting issue relating to the automated and low cost investing platforms (proliferating in this age of fintech). Per authors (emphasis is mine, throughout): "Technology has dramatically changed how retail investors trade, from placing orders using direct dial-up connections in the 1980s or Internet-based trading in the 1990s to the more recent rise of robo-advisers. With few exceptions, the introduction of these new technologies is generally associated with a decline in investor portfolio efficiency." In addition, "whether good or bad for investors, it is accepted that new technologies influence investor behavior". 

In this unique study, the authors used data that comes "from two large German retail banks that have introduced trading applications for mobile devices. For over 15,000 bank clients that have used these mobile apps in the years 2010-2017, we can observe all holdings and transactions, and, more important, the specific platform used for each trade (e.g., personal computer vs. smartphone). [As the result of having such a granular data over time] we can conduct all our main tests comparing trades done by the same investor in the same month across different platforms."

The authors present four sets of results:

  1. "First, we study if the use of smartphones induces differences in the riskiness of trades. Comparing trades by the same investor in the same year-month, we find that the probability of purchasing risky assets increases in smartphone trades compared to non-smartphone ones
    • "smartphone trades involve assets with higher volatility and more positive skewness. [Thus], smartphones increase the probability of buying lottery-type stocks by 67% of the unconditional mean for smartphone users."
  2. "Second, we examine the effects of smartphones on the tendency to chase past returns. We find that smartphones increase the probability of buying assets in the top decile of the past performance distribution. Smartphones increase the probability of buying assets in the top 10 percent of past performance by 12.0 percentage points (or 70.6% of the unconditional mean)." In other words, smartphones trades involve severe and pervasive biases in investor decision making.
  3. "Third, we investigate if investors selectively use smartphone to execute their risky, lottery-type, and trend-chasing trades. In this scenario, investors could simply substitute their trades from one device to another, without any real consequences for their overall portfolio efficiency. ....We find that, following the launch of smartphone apps, investors are—if anything—more likely to purchase risky and lottery-type assets and to chase hot investments also on non-smartphone platforms. ...this evidence potentially suggests that investors are learning to become overall more biased after their initial use of smartphones to trade."
  4. "...smartphone effects are stronger during after-hours (i.e. following exchange closure). Institutional differences between trading on official exchanges and in after-hours markets do not drive this heterogeneity. Given that individuals are more likely to rely on the more intuitive system [System 1-type] later in the day (Kahneman,2011), stronger effects during after-hours are consistent with smartphones facilitating trades based more on [intuitive] system thinking."

As an interesting aside, it is worth noting that the above results have nothing to do with the demographic biases or the potential lack of trading experience by smartphone-using investors. As noted by the authors: "German investors that adopt smartphone trading are, on average, 45 years old with nine years of experience investing with the banks."

Another aside is that authors also tested if the adverse effects of smartphones-based trading can be attributed to the first / early usage of these devices. It turns out not: "The effects of smartphones are stable from the first quarter of usage up to quarter nine or afterwards. The effects on volatility and skewness of trades, and probability of purchasing past winners are also stable over time."

To conclude: "Collectively, our evidence suggests that investors make more intuitive (system 1-type) decisions while using smartphones. This tendency leads to increased risk-taking, gambling-like activity, and more trend chasing. Previous studies have linked these trading behaviors to lower portfolio efficiency and performance. Therefore, the convenience of smartphone trading might come at a cost for many retail investors."

Ouch! Then again, this is fitting well with what we are observing happening in the markets these days: amplified herding, trend chasing, lottery-like speculative swings in investment capital flows, recency effects of overbidding for previously outperforming stocks and so on. 


8/1/20: Creative destruction and consumer credit


My new article for @TheCurrency_, titled "Creative destruction and consumer credit: A Fintech song for the Irish banks" is out. Link: https://www.thecurrency.news/articles/6150/creative-destruction-and-consumer-credit-fintech-song-for-the-irish-banks.

Key takeaways: Irish banks need to embrace the trend toward higher degree of automation in management of clients' services and accounts, opening up the sector to fintech solutions rather than waiting for them to eat the banks' lunch. Currently, no Irish bank is on-track to deploy meaningful fintech solutions. The impetus for change is more than the traditional competitive pressures from the technology curve. One of the key drivers for fintech solutions is also a threat to the banks' traditional model of business: reliance on short-term household credit as a driver of  profit margins.

"Irish banks are simply unprepared to face these challenges. Looking across the IT infrastructure landscape for the banking sector in Ireland, one encounters a series of large-scale IT systems failures across virtually all major banking institutions here. These failures are linked to the legacy of the banks’ operating systems."

"In terms of technological services innovation frontier, Irish banks are still trading in a world where basic on-line and mobile banking is barely functioning and requires a push against consumers’ will by the cost-cutting banks and supportive regulators. To expect Irish banking behemoths to outcompete international fintech solutions providers is equivalent to betting on a tortoise getting to the Olympic podium in a 10K race."



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.

10/7/16: Europe’s Banks: Dinosaurs On Their Last Legs?


Europe's banks have been back in the crosshair of the markets in recent weeks, with new attention to their multiple problems catalysed by the Brexit vote.

I spoke on the matter in a brief interview with UTV here: http://utv.ie/playlists/default.aspx?bcid=5026776052001.

Now, Bloomberg have put together a (very concise) summary of some of the key problems the banks face: "Europe's banks have been a focal point of investor skittishness since Britons voted to leave the European Union, but reasons to be worried about financial firms pre-date the referendum. Whether it be the mountain of non-performing loans, the challenge from fintech firms and alternative lenders encroaching on what was once their turf, or rock bottom interest rates eroding margins, the problems facing Europe's lenders are mammoth."

To summarise the whole rotten lot: European banks (as a sector)

  • Cannot properly lend and price risk (hence, a gargantuan mountain of Non-Performing Loans sitting on their books that they can't deleverage out, exemplified by Italian, Slovenian, Spanish, Portuguese, Cypriot, Greek, Irish, and even, albeit to a lesser extent, German, Dutch, Belgian and Austrian banks);
  • Cannot make profit even in this extremely low funding cost environment (because they cannot lend properly, while controlling their operating costs, and instead resort to 'lending' money to governments at negative yields);
  • Cannot structure their capital (CoCos madness anyone?);
  • Cannot compete with more agile fintech challengers (because the dinosaur mentality and hierarchical structures of traditional banking prevents real innovation permeating banks' strategies and operations);
  • Cannot reform their business models to reflect changing nature of their customers demands (because they simply no longer can think of their customers needs); and
  • Cannot succeed in their traditional markets and services (despite being heavily shielded from competition by regulators and subsidised by the governments).
Instead of whingeing about the banks' plight, we should focus on the banks' resound failures and stop giving custom to the patrician incumbents. Let competition restructure Europe's banking sector. The only thing that sustains Europe's banks today is national- and ECB-level regulatory protectionism that contains competition within the core set of banking services. It is only a matter of time before M&As and organic build up of fintech players will blow this cozy cartel up from the inside. So regulators today have two options: keep pretending that this won't happen and keep granting banks a license to milk their customers and monetary systems; or open the hatches and let the fresh air in.