London / £70000 - £90000
£70000 - £90000
UP TO £90,000 + BENEFITS
LONDON OR LEEDS OFFICE (HYBRID)
This role has a one-stage interview process so be quick to apply before you miss your opportunity to join this leading insurance company!
This insurance company is heavily driven by Data and Analytics and leading the market in their field so are looking for a ground-breaking pricing practitioner to join their growing team. There is a big focus on pushing and progressing staff up the ladder to let you grow with the company.
This position would be part of the wider pricing team. Within this role, you would be:
- Have the chance to work on a range of products such as motor, home, rescue, and pet insurance.
- Collaborating with other departments to bring a new data source to life and push insight from them.
- Constantly keeping competitive but realistic prices across all products by identifying opportunities to improve how risk is assessed to give a fairer price to customers and bring machine learning techniques in to automate this process.
- Manage the end-to-end modeling cycle while developing market-leading risk cost or retail models for the wider personal lines pricing team
- Focus on improving the pricing and underwriting performance and longer-term capability whilst maintaining pricing and underwriting discipline to maximise company-wide profits.
SKILLS & EXPERIENCE
- A strong knowledge of Python and SAS is essential.
- 2-3+ years of pricing experience is a minimum requirement preferably within the insurance industry.
- Knowledge of pricing model techniques and strong data analysis skills to push insights.
- Must have data science/machine learning experience such as building GBM models.
- Good written and verbal communication skills.
- Must have a strong numerate degree.
SALARY & BENEFITS
- up to £90,000pa base salary.
- Hybrid working (once a month in the office).
- Discretionary yearly bonus.
HOW TO APPLY
Please register your interest by applying directly to this advert on LinkedIn.
A new way to pay- Fintech innovation at the point of sale | Harnham Recruitment post
Instant transfers, real-time payments, virtual banks, and digital currencies – these are just a few of the ways fintech innovation has been booming in the last few years.
Around the globe, start-ups, upstarts, and non-bank payment providers have shaken up the banking status quo. New technologies, market conditions, and alternative business models fueled by global investment offer much needed change in payment systems as well as complement others already on the market. Demand for optimised payments experience in terms of speed, convenience, and multi-channel accessibility are the new ways to pay.How to pay- let me count the ways
Retail and traditional banking have moved away from slow batched processing as consumer demand drives real-time payment systems. This demand has Consumers in retail banking also benefitting from the development of payment systems that run in real-time rather than via the traditional (and relatively slow) method of batched processing. This demand has in turn furthered innovation in real-time payment infrastructures. Consumers no longer require a bank or credit card to make payments, but can instead use service layers that run on top of existing real-time payment infrastructures.
In our mobile world, mobile wallets are often at the forefront of thought for payment systems and with the rise of P2P payment such as Venmo, Square, and Klarna. While generally focused on the peer-to-peer (P2P), mobile capabilities are much smaller in the wholesale and corporate sectors. But, this won’t last for long. Projected smartphone growth offers banks an opportunity to adapt and consider solutions across devices to meet growing demand.
An increasing number of non-bank providers are entering the payments world as well. Consider the rise of digital currencies, foreign exchange and remittances, and other P2P models which enable users to buy and sell currencies directly at an agreed rate. Real-time technological innovation reduces currency risk faced by banks and money transfer agencies, while also lowering costs associated with money transfer.
Growth in e-commerce makes consumer and retail payments sector the fastest moving in terms of innovation and adoption of new payment capabilities. Renewed confidence in the financial services sector has led to a substantial rise in available jobs, particularly among risk management teams. Yet, professionals to fill these roles remain in short supply.Roll out the red carpet- these are the roles in high demand
Against the U.S., Japan, and globally, the U.K. faces a skills shortage in risk functions. According to a report by Accenture, over 75 percent of organisations say a shortage of core risk management talent impedes their effectiveness. Just over 70 percent are facing a shortage in new and emerging technologies. With an eye to the future, many organisations, capital markets, and U.K. banking plan to strengthen their understanding of emerging technology risk and their data management capabilities.
Roles in highest demand are those in counterparty credit risk, particularly within pricing. While more recently, graduates with quantitative backgrounds found roles in risk methodology, real-time payment structures and the role of e-commerce has created more opportunity for those who candidates who understand pricing models. Those at the first line of defence in regard to assurance, internal audit, IT controls, and cyber security fall within the scope of operational risk functions are also in demand.
The role of Brexit programmes will drive risk change hires in 2018. As negotiations become clearer, other organisations are expected to follow an investment bank in Canary Wharf which has made credit risk function hires a top priority. Top challenges in risk management function
Increased demand from regulators, increased velocity, volume of data, legacy technologies, and variety are the top challenges faced by U.K. banking and capital markets. To meet their needs, these organisations are focused on creating teams which blend core competencies, a deep understand of new digital capabilities, and commercial acumen.
Quantitative risk professionals with experience in counterparty and market risk analysis are in high demand as well as those with a pricing model focus. Demand for regulatory and portfolio level market risk managers have also seen an uptick in demand.
In order to overcome shortages, businesses are considering internal candidate pools and moving strong candidates between asset classes. Despite shortages of professionals with key skill sets within risk, employers have remained cautious. Quantitative risk roles are a notable exception, where skills shortages are most acute.
We have an opportunity for a Senior Credit Risk Manager within New Product Leadership to help build a leading Financial Service’s recently purchased Consumer Finance Portfolio. Shape the entire strategy, oversee all Scorecard and Model Development, and build your own team. Interested? For additional opportunities check out our current vacancies. Contact our UK Team at 0208 408 6070 or email email@example.com to learn more.
How Data Can Help In The Cost-Of-Living Crisis?
As we all know, knowledge is power, and increasingly industries are realising that decisions grounded in data are better decisions. In retail, consumer behaviour data helps to inform which products are being viewed the most online and in banking, transactional data can be used to identify fraudulent activity. The premise being, the more you know, the more that you can control.
As energy bills and food costs increase the overwhelming message for the individuals and companies facing surging costs is to ensure that you are aware of what is going in and out of your accounts. The logic is that without this knowledge, opportunities for potential savings may be missed.
But other than regularly checking a bank statement, what other information or data techniques can be used to inform decision-making and potentially cut costs for businesses and individuals?
As bills rise, energy is the word on everyone’s lips and technology is constantly developing that will help consumers to better track and control their energy consumption. ‘Smart homes’ for example – the term coined to describe households that have at least two forms of ‘smart technology’ such as smart meters or smart bulbs – are enabling users and suppliers to track household energy consumption and identify where it could become more efficient.
There are now 2.22 million smart homes in the UK, and the increasing digitisation and connectivity of devices, have only made homes smarter, with increasing numbers of household devices that can be monitored and controlled such as smart lights that can be dimmed, or switched on or off.
The Internet of Things (IoT) has enabled sensors and other measurement devices to speak to one another. Digitisation has packaged this into the accessible format of a mobile phone app where devices can be controlled, whilst automation technologies have reduced human error by using sensors to automatically turn off lighting when no one is in the room for example.
For businesses, Energy Management Systems (EMS) are becoming popular. These automation systems collect energy measurement data and make it available to users through graphics, online monitoring tools, and energy quality analysers. These systems can then automatically change the actions of the controlled device and facilitate the use of energy reduction measures, such as putting a device in sleep mode when not needed.
An EMS uses metering sensors that measure energy usage, a control system that transmits commands, and the actual controlled devices, such as air conditioning units, fans, or lights. A good example of a very basic EMS is the thermostat in your house, which has a sensor that measures the temperature in the room and a controller that tells the heater to turn on or off.
As consumers become more energy-savvy, the bank of data surrounding them – habits, consumption etc. is also building. Not only will this allow customers to see a pattern of their habits forming from current and historic data, but also gives opportunities for companies looking to offer competitive rates and more fodder for data analytics processes; think smart data that could allow policymakers to better understand how people use energy and how to reduce their costs.
There has been a huge amount of research into how this data can be used in the predictive modelling space. For instance, predicting the energy consumption of a building will allow owners to better plan ahead around peak times of consumption and it may influence decisions such as how many days you want to have your office open or your energy provider.
An efficient method for predicting electricity consumption in buildings is the use of ‘soft computing’ techniques. Such methods make use of data measured by sensors installed in buildings and inform optimised decisions and actions to save energy. For example, examining how a building’s design characteristics – wall, roof and window materials – are affecting its energy consumption by using sensors to detect heat loss through the roof.
Electricity load forecasting is another important tool – the accurate forecasts of commercial building electricity loads can reduce costs for companies by reducing electricity use around peak demand times.
Some researchers are looking to combine the predictive modelling of energy consumption with others, such as those around behaviour. A recent study looked at how lighting control in office buildings is driven by occupants’ demand for an indoor light environment.
However, due to the effect of glare, lighting control is often associated with shading adjustment. The study proposed a prediction model which can accurately describe the lighting and shading coupling control behaviour by fully considering the difference and diversity of occupants.
The future of using data to decrease costs for consumers and businesses will depend on how companies decide to use data analytics technologies to extract business intelligence going forwards. Businesses and individuals can easily purchase smart technology to monitor and control their data usage but enriching this data with complementary information will give deeper insights that could inform, for instance, the launch of a new service. As a case in point, PG&E has used SmartMeters to collect consumer energy-use data at hourly and daily intervals. The energy consumption data will supplement existing information on customers’ demographics, billings and payments, call centre reports and utility pricing, among other variables.
The company hopes to gain insights into how its SmartMeter platform might be used ‘to engage customers, reduce energy consumption and offer customers appealing alternative pricing schemes.’ Customers who participate in the program will have the ability to be notified by email, text message or phone when their utility use is moving toward a higher-cost tier.
Awareness around the importance of monitoring the energy consumption of your home or business, and the tools that can make it straightforward, needs to improve. And as data analytics continues to inform business intelligence, the energy-saving services yet be offered are no doubt going to be plentiful.
Looking for your next big role in Data and Analytics or need to source exceptional talent? Take a look at our latest Data jobs or get in touch with one of our expert consultants to find out more.
Using Data to Optimise Supply Chains
Using data to optimise supply chains
The ripple effects of Brexit and pandemic restrictions continue to affect industries that are heavily reliant on supply chains.
Many manufacturers are still struggling to make up lost ground. For example, the Baltic Airfreight Index (BAI), which tracks prices for transporting cargo by air, is still down approximately 40 per cent from its peak, as its supply chain continues to heal. In this environment, ensuring that supply chains are running as optimally as possible, and are flexible enough to cope with evolving developments, has become paramount.
Data has long been the bedrock onto which these industries build their processes. Without an accurate, comprehensive view of the entire manufacturing operation such as product quantities, timescales, and other logistical detail, it’s impossible for executives to make effective decisions. In a 2022 Industry Pulse survey, manufacturing and distribution executives highlighted the criticality of real-time intelligence in managing their supply chains under volatile business conditions.
Various new technology powered by data allows businesses to continuously review their processes, and adjust to the ever-changing landscape. This, in turn, will have a wealth of positive implications such as diminished costs, reduced waste, and improved profit margins.
The amount of manufacturing supply chain data available today is staggering. While most manufacturers have now begun harnessing their data, many are still struggling to capture significant value from it. A 2021 study revealed that just 39 per cent of manufacturing executives had successfully scaled data-driven use cases beyond the production process of a single product.
So, how can organisations harness their data to improve their supply chains?
Data can help increase transparency
One of the main challenges faced by supply chains is a lack of transparency. Supply chains often span across multiple manufacturing and logistics operators with several tiers of suppliers, and because of this, data is typically collected and stored in separate silos.
As a result, it’s difficult for supply chain managers to get a clear and holistic view of crucial KPIs, such as service levels and costs. This means information about the real-time performance of end-to-end supply chains is often unknown. Or, if it is known, it’s reported infrequently, which can impact business performance. For instance, the malfunction of remote equipment could remain undetected resulting in exploding supply chain costs and lead times.
Transparency can be increased by ensuring that more information is accessible and therefore usable. New technologies like the Industrial Internet of Things (IIoT), for example, can collect remote ‘process data’, which might include warehouse temperatures or transportation waiting times, via sensors and then forward this to the cloud in real-time to inform decision-making.
And with the cost of IIoT devices and sensors plummeting, and 5G connectivity expanding worldwide, manufacturers of all sizes have the chance to cash in on capabilities like tracking shipping containers on their journey. Thereby enabling them to set realistic customer expectations, schedule production activities dependent on the incoming shipments, and swap to alternate suppliers to overcome delays.
Data assists in strategic planning
The implementation of supply chain analytics, allows vital conclusions to be drawn from this real-time and supply chain data, allowing businesses to effectively plan ahead. This can be roughly categorised into four buckets:
- Descriptive analytics uses historical manufacturing data gathered from suppliers, and customers data, to identify important trends or patterns.
- Predictive analytics models out a range of ‘what-if’ scenarios by analysing a variety of macro-level data including consumer demand, weather events, and staff shortages to accurately predict how these may impact a manufacturer’s supply chain or production capabilities. All of which will ultimately inform the creation of a robust contingency plan.
- Prescriptive analytics uses the results of predictive and descriptive analytics to suggest potential actions that a manufacturer could take to achieve predefined goals. For example, identifying weak links in the supply chain.
- Augmented analytics harnesses Artificial Intelligence (AI) and Machine Learning (ML) techniques to analyse huge, complex data sets from multiple sources to make highly accurate predictions. One new application of augmented analytics is the improvement of worker safety by using wearable sensors that collect data on worker health, stamina, and exposure to occupational hazards and alert managers when interventions are needed.
So, what can analytical techniques support a business with?
Demand planning and forecasting/resilience
Predictive analytics supplements historical data with data on current market trends and industry competition allowing for improved demand planning and forecasting. In a nutshell, this means that manufacturers can better align production with customer demand, improving efficiency and reducing waste, as warehouses will only stock what is needed.
The agility of a business’s operations relies on the amount of information it has and how accessible it is. Take data from manufacturing systems – it can inform decisions to accelerate production, adjust output parameters, or enable proactive equipment maintenance, as and when required. Similarly for managing vendors, ‘Dynamics 365 Supply Chain Management’ can connect to supplier catalogues and enable near real-time visibility of supplier processes. This helps businesses to understand and control costs through priority-based supply planning, make AI-supported inventory decisions, and automate warehouse operations.
Proactive risk management
Complex supply chains pose a significant risk for manufacturers, just one key supplier being out of action due to adverse weather can easily cripple production, resulting in costly delays. To overcome this, manufacturers and suppliers can opt to share data, allowing manufacturers to analyse supplier data to gain deeper insight into quality, on-time performance, and pricing. This knowledge gives manufacturers greater insight into each link of their supply chain, allowing them to renegotiate pricing, address quality concerns, or switch to a more reliable supply partner.
Whether you are looking for your next opportunity in the data industry, or need to build out a data team to optimise your supply chain? Get in touch with one of our team today who will be able to help.
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