Brian Foster, Head of Industry Finance at Siemens Financial Services in the UK examines how flexible finance can help logistics providers meet the changing needs of retailers and consumers.
The upward trend of online shopping shows no sign of abating; research forecasts a 69 percent increase in parcel volume in Europe by 2021. UK consumers that utilise online shopping currently make 87 percent of their retail purchases online, up from 80% in 2017. Apart from the continued increase in volume, the nature of online retail is also evolving. Convenience of delivery is an important factor for consumers choosing where to shop. A PricewaterhouseCoopers (PwC) report shows that fast and reliable delivery would influence where almost a quarter of consumers choose to shop. Additionally, 40% of shoppers are willing to pay an extra charge for same-day delivery.
As consumers expect more from retailers, retailers will expect more from their logistics operators. Retailers that can offer speed and flexibility will win the customers and, therefore, the logistics companies that can facilitate that speed and flexibility will win the retailers. Consequently, logistics providers need to be ready to continually adapt and modernise to maximise cost efficiency and asset productivity, enabling their clients to meet consumers’ delivery expectations cost effectively.
Investing in new technology is key to achieving this. ‘The Internet of Things’ (IoT) offers future potential and creates opportunities for a connected transport system, where vehicles, goods and infrastructure communicate with each other. For example, “radio-frequency identification” (RFID) tagging can provide fleets with GPS and location data informing them of factors such as bad weather that might delay deliveries. Planning for these factors will enable carriers to deliver more goods on time. Similarly, data can be analysed to identify areas where efficiency can be improved; for example, identifying efficient drivers and routes. Smart location management systems can enable companies to easily track driver activities, vehicle location, and delivery status with real-time information, meaning that business processes can be streamlined.
Looking further ahead, the very assets which transport goods are also likely to change radically. Self-driving vehicles continue to be tested and could produce significant benefits for the logistics industry. Many companies are faced with massive driver shortages and the drivers make up about 30% of the road transport costs. Driverless vehicles can increase speed and flexibility of freight flows and, unlike drivers, can operate 24/7. 
Implementing new technology, however, takes time and investments in new vehicles and technology requires considerable capital expenditure. Tailor-made financing packages for the acquisition of a variety of new truck and trailer models are gaining popularity as a cost-effective investment-enabler. Asset finance can help logistics operators embrace new technology. Pay-to-use or access financing techniques such as leasing, and pay-for-outcomes agreements whereby the savings or gains made possible by a given technology fund monthly payments, are effective, alternative methods of funding equipment and technology investments and upgrades. Such financing techniques spread the cost of machinery over an agreed financing period, with monthly finance payments arranged to align with expected benefits gained over time from new/retrofitted equipment, such as improved productivity, operating cost savings, energy efficiency and access to new markets. This removes the need for a large initial outlay, thereby increasing the funds available for other expenditures. In other words, asset finance allows manufacturers access to the latest technologies, without having to commit scarce capital or use traditional lines of credit. Financing arrangements can cover other costs such as installation, as well as providing the flexibility to upgrade technology in line with technology developments.
Unlike traditional, generalist financiers that might lack comprehensive technical knowledge to fully evaluate the impact a potential investment can bring to a logistics operator, specialist financiers active in the transport arena understand the technology, its potential future value and its practical application. This comprehensive understanding of the financed equipment and technology enables specialist financiers to determine appropriate and tailored financing solutions that meet the company’s specific needs. Their expertise in equipment and technology and finance makes it possible for specialist financiers to assess the cost savings and/or expected benefits for the term of the agreement and factor that into the financing arrangement. Specialist financiers, moreover, can devise financing plans that cover a broad range of costs associated with using the equipment and technology, not just the cost of acquisition, meaning greater transparency regarding the expected operating costs for the customer. By using flexible financing, logistics companies have the opportunity to benefit from the investment in equipment and technology straight away rather than delaying their acquisition, and through that timely investment gain an important competitive advantage.
Internet shopping continues to become more popular and consumers are becoming increasingly demanding. Logistics companies need to be ready to help retailers meet these demands by investing in technology that enables them to be more efficient. Technology is developing rapidly and holds many exciting possibilities for the industry. But the transport sector operates under tight margins and investments must be made sustainably. Operators that don’t meet this challenge risk being left behind.
 Logistics Manager, ’Huge growth in urban logistics space vital to meet online demand’, 18 October 2017 https://www.logisticsmanager.com/huge-growth-urban-logistics-space-vital-meet-online-demand/
 Net Imperative, ’Online shopping in the UK up 9% in a year’, 11 September 2018 http://www.netimperative.com/2018/09/online-shopping-in-the-uk-up-9-in-a-year/
 PricewaterhouseCoopers, ’Signed, sealed, delivered (and regularly returned)’, 2018 https://www.pwc.com/gx/en/industries/consumer-markets/consumer-insights-survey/delivery-expectation.html
 Indigo, ’How are logistics operations adapting to the increased popularity of Internet shopping?’, 24 January 2018 http://www.indigo.co.uk/article/2018/1/24/how-are-logistics-operations-adapting-to-the-increased-popularity-of-internet-shopping
 Iot for all, ’What’s Ahead for IoT and Logistics in 2018’, https://www.iotforall.com/logistics-and-iot-trends-2018/
 Innovation Enterprise Channels, ’The top six IoT applications in logistics’, 13 July 2018 https://channels.theinnovationenterprise.com/articles/how-the-internet-of-things-will-revolutionize-the-logistics-industry
 Sabinext, ’Self-Driving Vehicles: The New Reality for Logistics?’, 17 June 2018, https://sabinext.com/self-driving-vehicles-new-reality-logistics
ENTERPRISE BLOCKCHAIN: DRAGGING INSURANCE OUT OF THE DARK AGES
Ryan Rugg, Global Head of The Industry Business Unit at R3
The history of insurance traces back to the development of modern business and insuring against its risks; property, cargo, medical and death. Insurance helps mitigate losses, wary of the financial losses a capsized ship could cause, forward-thinking vessel owners established communal funds that could pay for damages to any individual’s ship within the group. While this basic concept holds strong to this day, insurance is now a multi-trillion dollar industry that impacts almost every other sector of business, from healthcare to capital markets and aviation.
Despite the insurance industry’s image of being a conservative sector, insurers have been consistently innovative in the property and perils they protect against, but the supporting technologies and infrastructure have remained antiquated and unfit for purpose. Operational inefficiency is the single biggest threat facing the insurance industry today, and insurers are now taking steps to tackle this challenge head-on with purpose-built enterprise blockchain technology.
Inefficiency and fragmentation
Blockchain provides a solution to drive efficiency and security that would allow private data to be shared in a secure manner. Many policies are still sold over the phone rather than online, and the policies themselves are then processed on paper contracts, introducing huge potential for manual errors in claims and payments. This anachronistic infrastructure is even more surprising when you consider the complexity of the insurance ecosystem and the amount of parties involved in a transaction, including consumers, brokers, insurers, reinsurers and more.
The costs of this inefficiency and fragmentation are well documented. Inaccurate, disparate sources of data acquisition lead to long underwriting cycles and inaccurate risk profiling. Extensive manual intervention is required across the insurance value chain, ranging from contract placement to claims settlement. Archaic billing systems and complex billing processes lead to high reconciliation costs. Ambiguity in loss conditions, assessment procedures and claim settlement delays leads to increased litigation risk. It has been estimated that as much as 60% of customer premiums is consumed by these inefficiencies.
In addition, increasingly stringent and dynamic regulatory requirements continue to impact areas such as renewals and claims assessment. Insurers often have a complete lack of visibility of their liabilities and obligations, and a lack of transparency across the entire business. In today’s regulatory climate, it is unsurprising that authorities are beginning to demand more from insurers.
Blockchain technology is not a panacea for all of these problems, but with the right architecture a platform can address and reduce inefficiencies. There are also new revenue and growth opportunities in cutting-edge sectors such as cyber insurance that blockchain technology can help enable.
Tackling the blockchain privacy challenge
Blockchain offers insurance firms a new way to coordinate information between each other, by using a pre-agreed technology solution instead of relying on a third party’s bookkeeping. The technology enables disparate parties to connect via a shared platform environment. While this premise may appear simple at first glance, the insurance industry has specific requirements in relation to privacy and security that only certain blockchain platforms can fulfil.
For example, if a blockchain has the appropriate data privacy architecture in place, each insurance firm can maintain the same amount of control over their data as today, but with more flexibility. Unlike the traditional permission-less blockchain platforms – in which all data is shared with all parties – Corda shares information with those who have a “need to know,” ensuring the confidentiality of trades and agreements while also capturing the benefits of a shared distributed ledger infrastructure.
Blockchain platforms such as R3’s Corda have been purpose built for enterprise usage in industries such as insurance and tackle issues such as data privacy, scalability and security head-on. Following a period of experimentation with multiple consortia and technologies, insurers are now consolidating their blockchain efforts around Corda.
Testament to this is the recent decision of the industry-leading B3i consortium to port from IBM’s Fabric to Corda or RiskBlock decision to port from Ethereum. All the major insurance groups and ecosystems are coalescing on Corda in order to effect change and form standards. As Metcalfe’s Law states, the value of a network is proportional to the number of connections in the network squared – the more insurers that build upon on a common platform, the more valuable the platform becomes to all participants due to the interoperability of applications. The consolidation around Corda creates network effects industry-wide.
Contract placement: leveraging the network effect
To more tangibly examine the benefits of these network effects, we can look at a specific insurance use case that involves a network of many different entities and counterparties – contract placement.
Contract placement is the process of negotiating a potential insurance contract between a broker and an insurer in order to issue the contract to provide coverage for an end customer. For most commercial and specialty insurance scenarios, except for small commercial and some mid-market products, this is an arduous, complex process involving several entities – a broker, one or more insurers, and potentially a reinsurer and reinsurance broker. Furthermore, outsized risks generally mean that multiple insurers come together to insure the risk at the requested limit price, resulting in additional complexity for the broker in managing the placement process.
Contract placement, with the extensive negotiation cycle between a broker and insurers, as well as between an insurer and reinsurers – with or without a reinsurance broker thrown in – has several inefficiencies related to inter-firm coordination. Extensive manual intervention and reconciliation is required for brokers, insurers and reinsurers to keep track of requests and responses; high IT spend is required for all participating parties to maintain an audit trail of the negotiation history between different entities; and each firm must make heavy investments in document storage systems to maintain separate contracts over the policy lifecycle.
Leveraging the network effect by connecting brokers, insurers and reinsurers onto the same blockchain platform can deliver numerous benefits. These include:
- Near-instantaneous communication between participating parties to eliminate delays associated with reconciliation and coordination;
- Real-time consensus among all parties involved in the contract on coverage, price, terms and conditions;
- Complete audit trail from all sides of negotiations and data exchanges;
- Greater regulatory compliance throughout the insurance industry due to instantaneous communication of in-force contracts to the regulator;
- Eliminating the “double spend” problem of having the customer buy the same policy from different insurers by involving the notary (regulator);
- Reduced IT spend for individual firms, with eventual decommissioning of legacy document storage systems and reducing spend on document generation systems.
A brighter future
Blockchain technology offers great promise across many avenues, not only contract placement. Platforms like Corda can add value to many insurance business segments – commercial and specialty insurance, life insurance, personal lines and health insurance, along with niche areas like marine and trade credit.
The industry’s recent consolidation around Corda reaffirms that data privacy is pivotal for a network of enterprises and that the platform’s peer-to-peer data sharing approach matters for insurance blockchain applications going into production. For a highly regulated industry like insurance, only Corda can ensure that the entire supply chain of brokers, insurers, reinsurers and consumers can interact in a seamless, secure and private manner.
From contract placement to insurance as an industry, we are excited to see the new opportunities and efficiencies that blockchain technology will enable between this wide ecosystem of participants now that the right network – Corda – is in place.
TWO TO TANGO? MARKET DATA AND OPINIONS IN INVESTMENT MANAGEMENT
Sebastien Lleo is Associate Professor of Finance at NEOMA Business School (France)
Analyst views and expert opinions matter. They are an invaluable complement to market data when it comes to formulating relevant capital market expectations and to strengthening risk management models and practices. But watch out for behavioral biases!
“Garbage in – garbage out!” Every investment management professional has heard the warning that poorly formulated capital market expectations will get portfolio optimisers to produce inefficient, unrealistic, and even outright dangerous portfolios.
Thus, considerable efforts have taken place to turn available economic and market data into accurate capital market expectations. These lead to the development of slick statistical methods, effective econometric techniques, and powerful machine learning algorithms.
Opinions can also be an invaluable source of insights to construct accurate capital market expectations.
What are the types of opinions on financial markets?
Opinions take multiple forms in financial markets. They include analyst views, opinions from political and economic experts, super forecaster predictions, and investor polls.
Moreover, opinions abound on financial markets. Consultancy Quinlan & Associates reported that the bigger banks and brokerages emailed over 40,000 pieces of research every week in 2016, despite continuing job cuts in the financial sector. Social media also contribute to the spread of opinions: according to the financial website Modestmoney.com, there are at least 839 active financial blogs published in English.
Why should I use expert opinions?
Opinions have three key benefits.
First, opinions can be a crucial complement to traditional economic, corporate and financial market data to construct realistic capital market expectation, and keep those up-to-date. This statement is especially true in times of heightened uncertainty, such as market bubbles and financial crises, when traditional data fail to provide an accurate assessment of market conditions.
Second, opinions can strengthen risk management models and practices. Opinions can widen the range of scenarios considered in portfolio optimisation and risk management. Dissenting opinions provide a cornerstone for the construction of meaningful stress test scenarios.
Third, we can use opinions, even when traditional data are not. For example, assessors evaluate insurance claims, and appraisers estimate the value of illiquid assets, such as real estate and collectables, periodically.
How easy is it to collect opinions?
The inclusion of opinions requires extreme care.
Let’s look at analyst views and expert opinions. We all know that not all experts or forecasters are equally accurate. A widely reported study by CXO Advisory Group LLC tracked 6,582 forecasts for the U.S. stock market published by 68 experts between 2005 and 2012. The study found that average accuracy across experts was 47.4%, with individual accuracies ranging from a low of 21% to a high of 68%.
Therefore, investment management teams need to implement a process to guarantee the relevance of the opinions used in their models. This process, known as “elicitation,” is described in abundant literature. The books by O’Haghan (2006) and by Meyer and Booker (2001) are an excellent place to start. Essentially, the elicitation process helps to construct views that are specific, explicit, and structured. Opinions need to focus on a specific variable or parameter, such as the price of a given asset or the mean of a distribution. Opinions need to explicitly provide a mid-point or most-likely scenario, a confidence interval, and to relate the confidence interval to a probability distribution. Finally, opinions need to be structured to provide a transparent and auditable trail.
What are the implementation challenges?
Three main implementation challenges need addressing.
The first and most dangerous challenge is that opinions are often subject to the behavioral biases. Behavioral biases, in particular overconfidence, excessive optimism, conservatism, confirmation bias, and groupthink play an essential role in how finance professionals perceive and process information, and on how they form their forecasts. Recently, in a simulation study, Davis and Lleo (2020) recently found that the presence of biases explained nearly 70% of excess risk-taking. Therefore, it is crucial to debias forecasts before using them in any model.
Second, expert opinion models are Bayesian and therefore require the specification of a prior distribution. We can overcome this difficulty with some original thinking, as with Black and Litterman’ reverse optimisation exemplifies.
Third, aggregating of multiple expert opinions is considered an essential conceptual and computational problem because it requires engineering a joint distribution out of a collection of univariate distributions.
How can I integrate opinions in my portfolio selection model?
Currently, several families of portfolio selection models use opinions as input. The best-known and oldest is the Black and Litterman (1992) model, which uses analyst views to generate capital market expectations in a Markowitz-style single-period optimisation framework. This approach has been extensively discussed and developed in a large number of subsequent papers and chapters.
However, the Black-Litterman approach has two fundamental limitations. First, it is static, meaning that it locks portfolio managers into a “buy-and-hold” strategy, ignoring the possibility that portfolio managers may shift their asset allocation as financial market conditions change. Second, it ignores the presence of behavioral biases in expert opinions.
To address the first limitation, Frey et al. (2012) and Davis and Lleo (2013,2020) proposed two closely-related dynamic portfolio management models. Although both models are developed in continuous time, we can transpose them to a multiperiod discrete-time setting.
The second limitation has proved more elusive. At the moment, Davis and Lleo (2020) is the only dynamic portfolio selection model that addresses for behavioral biases.
Black, F., Litterman, R., 1992. Global portfolio optimisation. Financial Analyst Journal 48 (5), 28–43. Davis, M., Lleo, S., 2013. Black-Litterman in continuous time: the case for filtering. Quantitative Finance Letters. 1 (1), 30–35.
Davis, M., Lleo, S., 2020, Debiased expert forecasts in continuous-time asset allocation. Journal of Banking and Finance. 113.
Frey, R., Gabih, A., Wunderlich, R., 2012. Portfolio optimisation under partial information with expert opinions. International Journal of Theoretical and Applied Finance 15 (1). O’Hagan, A., 2006. Uncertain Judgments: Eliciting Expert’s Probabilities. Wiley.
Meyer, M., Booker, J., 2001. Eliciting and analysing expert judgment: a practical guide. ASA-SIAM Series on Statistics and Applied Probability. Society for Industrial and Applied Mathematics.
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