By Willem van Enter, Vice President EMEA, OutSystems
We all use software applications every day, all the time. That part should make sense to everybody. With many of us now happy to call ourselves digital natives, the question is not whether we are going to use apps to make our lives better; it is now a question of which apps we will choose to build our personal workflows around.
This ubiquity of software penetration is a good thing. It allows us to automate our work (and indeed personal) lives in a manner that we may never have considered, even as recently as the turn of the millennium.
But there’s a bigger challenge here.
More users need more apps in more places with more functions spanning more data sources connected to increasingly complex analytics engines, and all of that software has to be deployable across an ever greater number of device form factors and platforms.
Once an IT shop is empowered with low-code efficiency, the speed of development and release can rise sharply. But no business should expose themselves to this level of power without first thinking about all the control mechanisms needed to be able to accommodate new low-code-created apps.
Policy, provenance & policing
We’re talking about areas such as user provenance checks (so that we know who built which piece of software and if they were supposed to), policy controls (so that we know which software is accessing which data sources and whether it is supposed to) and areas like scale-provisioning (so that an organization’s IT estate can cope with a much higher throughput of information) and so much more.
The move to taking advantage of low-code software development is happening already. But, for enterprise organisations large and small to truly take advantage of the efficiencies it offers, they need to have faith in the ability of any platform’s ability to ultimately deliver workable, serviceable, functioning enterprise-grade software.
They need, to coin a phrase, to know that low-code makes the grade to enterprise-grade. So, what elements of core form and functionality should they look for?
Making the enterprise-grade grade
Building secure enterprise-grade low-code software is imperative; obviously, it is. Secure software development in this space is so fundamental that efficient low-code platforms will always be presented with security controls as an inherent and implicit part of their core functionality.
Nobody expects business applications designed to serve potentially millions of users with digital experiences to let them down, so enterprise-grade security, scalability, governance and performance should form key elements in the platform and toolsets that are used.
Because low-code is typified by a high degree of automation, an effective low-code approach should offer hundreds of automatic security and risk controls in its portfolio. But implementation is just the first step; an always-on monitoring and operations source also needs to exist for the customer to be able to assess their risk factors at any given time.
Climbing the scalability peak
Enterprise-grade low-code software may start off as an experimental application or some level of prototype or test case. Its speed of development naturally gives rise to its use in this type of development. But when an application (or some other code-based data service) hits the spot, the team behind it will need to know that it can scale.
Let’s say a small medical tech lab develops an application that helps track some aspect of disease outbreaks that takes a radically new approach in some way. If a viral pandemic ensues, then that software would need to scale seamlessly from something smaller than departmental level to an Internet-wide deployment – all without rewriting any code or hitting a wall.
Climbing the peak to true enterprise-grade scalability with low-code software involves taking advantage of technology that includes containers and microservices. Only by ‘thinking small’ in this sense can you consider being able to ‘think big’ later on and build mission-critical apps that scale to support millions of concurrent processes.
Within all of this discussion, it will be crucial to keep an eye on governance so applications built with low-code platforms can comply with controls such as GDPR, Sarbanes-Oxley, PCI, FedRAMP and more. The proven way of doing this is to use low-code development tools that offer a fine-grained control of your software portfolio with the ability to perform dependency checking, audits and validation.
There’s a human factor here, too, i.e., organisations can rely on low-code automation advancements for a lot, but they also need to think about establishing teams that can work simultaneously and keep conflicts to a minimum.
Finally, let’s mention performance. It’s a key measure of how and why any piece of software was developed in the first place. Software needs to work, it needs to drive business forward, and it needs to do so at a pace that is commensurate with and proportionate to the use case requirements behind why it was developed in the first place.
In the low-code universe, we have the ability to deploy enterprise applications that are automatically optimized to ensure they perform as designed and expected. We also have the ability to use pre-built connectors that integrate with automated enterprise logging technology, which gives developers real-time performance monitoring feedback to help avoid possible bottlenecks.
Low-code software application development can offer all of these features, controls and characteristics, so organisations can be assured that low-code does make the grade for enterprise-grade. All that’s needed is for the customer themselves to know how high low-code can go to be able to graduate to this new grade of efficiency.
FROM MANUAL TO MACHINE LEARNING: HOW TO APPROACH THE RECONCILIATION ‘PROBLEM’
By Christian Nentwich, CEO at Duco
At the start of 2020, before the global coronavirus pandemic changed the world, financial industry experts recognised that this would become the ‘decade of data’, with firms inundated with trillions of lines of data from a multitude of sources.
One of the many effects the current crisis has had is to amplify the need for resilient, connected systems and more robust processes. With business continuity front of mind, many organisations are looking for more efficient ways to manage huge swathes of data from multiple, disparate sources quickly and accurately. Data integrity is a key concern, and many are asking how they can automate their most critical processes.
However, despite the rush to digitalise many manual systems, automating reconciliations is still one of the toughest areas to crack. Even pre-pandemic, automating this essential control function in financial services – which can help eliminate operational risk that can lead to fraud, fines, or in the worst case, the failure of a firm – was proving elusive for many organisations. Why?
Many organisations are facing a situation where there are a multitude of systems, different processes, technology types and computing. Within that, there are three key reasons that make automation difficult:
- A lack of standardisation – In many cases in financial services there are no strict data standards. For example, different counterparties provide trade and position data in different formats. Each one requires a bespoke reconciliation process or expensive data normalisation.
- Increased complexity – Cash or stock assets can be matched on a few basic fields, but for more complex products you need to take far more information into account. Most current systems are unable to deal with every asset type that crops up in a timely manner. And, that’s before we get to the range of data needed for regulatory reporting, and the associated reconciliations required.
- Poor data quality – The enemy of automation. Missing fields, inconsistent coding schemes and unavailability of common keys make automation difficult when using current solutions due to hardcoded assumptions within those systems.
However, in a world where the quantity and complexity of data that firms need to handle is set to increase exponentially, relying on manual systems and processes is no longer feasible. So, how do firms deal with this influx of data in the most intelligent way?
We recently launched ‘The Reconciliation Maturity Model’, a new roadmap that will help financial firms improve the automation, efficiency and integrity of data across all reconciliation and data matching tasks. The model guides reconciliation practitioners through five key stages of reconciliation maturity, from ‘manual’ through to ‘automated’ and eventually ‘self-optimising’ – where machine-learning technology automates nearly the entire process, and where intersystem reconciliations are all but eliminated
Importantly, a more progressive approach to reconciliation automation will not only result in greater operational efficiency, it will also dramatically boost operational resilience, and put forward-thinking financial institutions in a better position to benefit from new technology and the added insight that intelligent systems bring.
The five stages of reconciliation maturity are:
- Manual – By this we mean using Excel or some other form of spreadsheet, macros, home-grown applications or – in some instances we’ve come across – printing out sheets of paper and marking inconsistencies with a highlighter pen! However, as the organisation grows, and the data becomes more complex, the risk of error skyrockets. There’s no audit trail, no governance and it becomes increasingly expensive to scale. If in the 2020s you’re throwing an increasing number of bodies at a data matching exercise, you know something’s wrong.
- Hybrid – For the majority of organisations, this takes the form of a point solution, usually deployed to automate high volume, low complexity reconciliations such as cash or custody. These point solutions – by their very nature – tend to specialise in a certain type of reconciliation. Firms trading a wide range of assets, or those dealing with complex data, may need to use multiple point solutions to handle different reconciliation types. However, there will be many reconciliations that these point solutions are not able to handle elegantly. In these cases, firms tend to fall back on manual processes. The result is a patchwork quilt of different reconciliation approaches stitched together by manual work. The whole process is costly, difficult to keep track of, and difficult to scale.
- Automated – All reconciliations are consolidated onto automated systems, and small teams build and onboard reconciliations, and oversee exception investigation.The key to getting to this stage is using the right technology. To reach Stage 3, firms need to be able to onboard reconciliations in hours or days, not weeks or months. They need to be able to rely on agile, flexible technology that can deal with complexity without multi-week data transformation projects. Once this technology is in place, complexity and risk can be vastly reduced, while increasing efficiency and transparency across processes.
- Improving – This enables greater efficiency and oversight of the reconciliation function as a whole. It also enables firms to normalise their data across the business and implement additional data quality checks across systems, highlighting areas of incomplete or incorrect data. Organisations are then able to start consolidating systems and removing duplicate reconciliations which have already been handled upstream. Processes become leaner, more efficient and more transparent.
- Self-optimising – Full automation is deployed across the entire lifecycle of reconciliation, from onboarding to exception resolution. There is very little involvement from staff and continuous improvement is possible via a machine-learning enhanced system. Internal reconciliations are removed, leading to major reduction in cost and complexity.
While stage five is the ‘holy grail’ that all financial organisations should be aspiring to, many firms are at the ‘hybrid’ stage, and making the leap to ‘automated’ is the most challenging step. However, once at stage three, firms are more able to move up the process to ‘self-optimising’. At this point, with enough training data, machine learning can spot errors, outliers and poor data quality at source, reducing the number of reconciliations required.
So, while we know that moving from manual to machine learning is not an overnight process, The Reconciliation Maturity Model provides a blueprint to getting there.
The Reconciliation Maturity Model is available for download here https://content.du.co/reconciliation-maturity-model-whitepaper
THE BEST PATHS TO SECURE AUTO FINANCING IN 2020
The previously flourishing economy has taken some dramatic turns in the last few months due to the health and economic fallout from the global Covid-19 virus. Many industries, including auto lending and financing, have seen a shift in demand. Due to economic uncertainty, many individuals are putting off the risk of making major expenses in the wake of this pandemic.
Consumer confidence is relatively low compared to the last 3-5 years . The current strain of the pandemic on the economy has seen many would-be car buyers postpone on making the purchases.
J.D Power recently revised its prediction that car sales could see up to a 3 million unit decline this fiscal year. Although the slow down in car sales is inevitable, carmakers and sellers have come up with new servicing tools to cushion and attract more car buyers amidst this Covid-19 pandemic. Below is a compilation of the best paths car buyers can use to secure auto financing in 2020.
Coronavirus Car Payment Programs and Plans
To the relief of consumers looking to buy a vehicle amidst this pandemic, most of the major car manufacturers have rolled out favorable car payment plans. These plans have been devised to cushion individuals whose income has been terminated or is threatened by the ongoing health crisis.
On the other hand, car manufactures are keen to avoid the double-whammy of increasing car repossessions and plunging new car sales. Despite the history of car sales recovering in time, car makers cannot wait for tides to turn because of the current high economic uncertainty. Below are some of the most comprehensive and aggressive car payment plans that major carmakers have devised in favor of new car buyers.
- GM is currently offering certain models with 0 percent financing for 84 months. The deal gets even better as car buyers can defer payments for as much as 120 days. All GM customers are eligible for a 3GB in-car Wi-Fi package.
- Ford is also offering to cover the first three months of payments in a six-month payment relief plan. Payments can be deferred for 90 days and the purchase of new cars is available online.
- For ‘well-qualified customers’ making their payments through special APR, Nissan is offering a 90-day payment deferral plan to new customers. The carmaker is offering a 0 percent financing deal on select models.
- Hyundai said it will cover a total of six-month payment for car buyers who lose their jobs or income due to the ongoing Covid-19 pandemic. The offer is available for customers leasing or buying new cars.
- Lexus and Toyota will allow its customers to defer payments for 90 days although the interests will continue to accrue during the deferral period.
- Customers looking to buy Honda cars will get as much as $1000 off while Acura buyers can expect $500 off.
- Subaru customers have no payment deferral option but can expect a 0 percent financing plan with qualifying credit.
Coronavirus to Cause Delayed Deliveries
The global supply chain has suffered great disruption caused by the pandemic. New car buyers should expect a delay in the delivery of their purchased cars. Supplier factory closures in countries like China could see even the yet to close manufacturers experience delays in operations.
Are Car Dealers Currently Open?
Even in localities with strictest policies, car dealerships are not being asked to close. But some car dealers may opt to voluntarily close shop due to the risk of employee infection. Vehicle leasing and rentals, as well as car service and parts businesses, were included in the list of essential services exempted from closure orders. Car sales can still be operated under strict health precautions.
Does Buying A New Or Used Car Put the Buyer at Risk of Infection?
No. There have been no specific reports linking significant risk of infection or exposure arising from the purchase of new cars. However, consumers should take precautions such as using face masks, hand sanitizer, and social distancing protocols. Generally speaking, in this climate it is wise to be considerate of others who may be at a higher risk than you.
With many dealerships adapting to the coronavirus situation by offering contactless car buying and favorable financing options, now would be a great time to purchase a new vehicle. Not that you won’t face some hurdles along the way, but the current storm has created great opportunities and offers such as home delivery and competitive car loans.
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