Francesca CCO at Axyon AI
Since the outset of the pandemic, the global economy has experienced much uncertainty. Although many people expected this year to be another good year for the stock market as the world economy continues recovering from the crisis, fears over the recent developments of the Omicron variant, together with unclear messages from central banks, have caused more instability. This market volatility presents fund managers with significant operational and performance-related challenges and a potential retention issue in the face of further volatility due to new variants and unexpected developments. So, how can AI and machine learning help investors prepare for this volatility and demonstrate resilience to their clients?
AI’s transformative role for asset management firms
Ultimately, AI and machine learning enable fund and asset managers to gain valuable time to adjust risk and protect investments with the intrinsic value of predictive analytics. It can also help businesses navigate challenging conditions by detecting anomalies in the market before any crisis occurs. By implementing AI, fund and asset managers can also monetise data and improve automation from the front to the back office.
AI vs. traditional models
While nobody can predict the unpredictable, market disruption is often on the cards. Many investors have lost confidence in asset managers who managed portfolios using traditional quantitative models, as they struggled to keep up with volatile market conditions. As such, funds need to find a way to better mitigate the risks with powerful predictive analytics of fast-moving markets and avoid losing investor confidence. In today’s world, relying on traditional portfolio management models when a market crisis occurs can result in the investments becoming severely impaired and can push a large amount of chaotic data into quantitative models. Therefore, asset managers need a system that can account for volatility and manage expectations more accurately.
Traditional portfolio models are built around strong assumptions on the behaviour of underlying assets, measuring normal distribution patterns on linear scales. As a result, they find it difficult to cope with the flood of chaotic data into their systems caused by high levels of volatility and fund managers’ ability to accurately analyse and predict where the market would go next and navigate through the crisis was significantly limited. Throughout the COVID-19 pandemic and consequent volatility, businesses with these models have had their limitations exposed.
Advanced AI systems are unrivalled from portfolio management to risk management for example detecting anomalies in the market. AI models can handle large and chaotic sets of data learning from the past the actual relationship among variables. Moreover, the application of advanced analytics to these data sets may also provide more real-time insight into the risks related to these shocks for the stock market.
Unlike these traditional models, AI systems are completely agnostic about markets and their associated risks, meaning that they can be trained to sound the alarm when the structure in the data is anomalous, and therefore could be a sign of an upcoming unpredictable event. These AI-powered tools can be also used to read the reality of the situation at hand, without any pre-ordered rules.
By strengthening a model against chaotic data, AI allows fund managers to see non-linear, complex patterns in asset behaviours that can be captured, and make market view predictions at a higher level, no matter how changeable conditions become.
Turning Point: The pandemic as an opportunity for change
The pandemic is still an opportunity for new technologies to prove their merits and show that AI and machine learning can offer a better way to use data and quickly adapt to the ever-changing ‘new normal’. By modelling the potential repercussions of major geopolitical, financial, or environmental events, businesses will be better placed to adapt, reposition, and overcome the obstacles the pandemic presents.
We have seen that investment in technology and data infrastructure is working its way up asset managers’ agendas, and AI and machine learning has by no means reached its limits. Advancements in technology will mean improvements to business performance will continue, and due to the wealth of data already stored in most financial institutions, there is great potential to build on the success of previous solutions. Businesses who are late to harness the superior analytical power of AI will likely find themselves trailing behind the competition. Implementing these innovative machine learning technologies will undoubtedly be a powerful solution to the problem of meeting and exceeding investors’ expectations of mitigated risk and higher returns.
Accounting Automation in the Future
Accounting automation is the process of streamlining repetitive tasks in financial processes. For example, some processes like invoicing are time-consuming and repetitive. Automation can reduce manual labor and save businesses both time and money. Also, it helps improve accuracy, reduces errors, and provides more accurate financial reporting.
Accounting automation in the future will be increasingly important for businesses to stay competitive. But every new change comes with both advantages and challenges. Let’s dive in to get ready for this future trend.
Potential Future Benefits of Accounting Automation
Increased Efficiency and Cost Savings
Accounting automation is a great way to increase efficiency and cost savings. For example, AI bookkeeping uses advanced algorithms to automate many accounting tasks. So, companies can track expenses, prepare financial reports, and more using AI.
It reduces the time needed for manual entry. So, businesses can spend fewer labor hours on tedious processes. They can increase efficiency by freeing up resources for more strategic work. It also helps reduce errors and inconsistencies associated with manual processes. So, the cost of compliance is lower because of greater accuracy.
Improved Accuracy and Reliability
Accounting automation can improve accuracy and reliability in accounting processes. For example, Automating bank reconciliation is less prone to errors from human mistakes or miscalculations. You can automate the process to identify discrepancies between the bank statement and accounting records. It helps to ensure that financial reports remain accurate and reliable. So businesses can take corrective action faster than processing data manually.
Streamlined Business Processes
Streamlined business processes involve eliminating unnecessary steps, reducing paperwork, and automating repetitive tasks. This allows businesses to focus on higher-value activities, such as developing new products, improving customer service, and developing strategic plans for the future.
Making a Better Decision
Accounting automation can enhance decision-making in 3 ways.
1. It enables businesses to access real-time information from multiple systems. So they can identify trends for better decision-making.
2. Automated accounting also helps with forecasting, budgeting, and auditing tasks. It enables businesses to be more proactive in their decision-making processes.
3. Also, automated accounting tools can integrate with enterprise resource planning (ERP) systems. They can manage data across the enterprise and make concise decisions that are favorable to the company as a whole.
Increase Customer Satisfaction
Accounting automation can help businesses increase customer satisfaction by streamlining their processes and providing a more efficient customer experience. For example:
4. Automated accounting systems can automate tedious manual tasks such as invoicing, data entry, and payroll processing. This allows businesses to focus on other aspects of their operations that are more important for customer service.
5. Automated accounting systems can also provide customers with more accurate and timely financial information. The information can help them make better decisions about their finances.
6. Also, accounting automation enables businesses to respond quickly to customer inquiries. It helps reduce wait times and improve the overall customer experience. So, you can build better relationships with their customers.
Accounting automation takes place online or comes with cloud-based solutions. So, you can access your information and do your job from anywhere instead of being confined to one spot.
Challenges to Implementing Accounting Automation in the Future
Cost of Technology Infrastructure Upgrades
Automating an accounting system often requires businesses to invest in new hardware and software, such as servers and other associated equipment. These upgrades come with a hefty price tag that may be difficult for small businesses to afford.
There are also extra costs, such as installation fees, setup charges, software licensing fees, cloud storage costs, and maintenance fees.
Training Requirements for Staff Members
Accounting automation involves using advanced technology to automate certain processes. So, it creates a need for trained staff members who can handle the new technology. Training requirements vary depending on the type of software used.
Some common training includes record-keeping procedures, software applications, and troubleshooting skills.
Regulatory Compliance Issues
Accounting automation can be a time-saver, but it also requires firms to be aware of the applicable rules and regulations. Companies must ensure that their automated systems are compliant with relevant laws and regulations such as Generally Accepted Accounting Principles (GAAP), International Financial Reporting Standards (IFRS), and other applicable accounting standards.
Besides, they must also comply with legal requirements related to taxes, financial statements, and other reporting obligations.
So, businesses must consider the complexities of regulatory compliance when automating accounting.
Security and Data Protection Concerns
As businesses move their accounting processes to the cloud, they are exposed to a wide range of potential security risks. Data breaches can cause significant damage to the business’s financial and reputational integrity. Besides, the complexity of automated accounting systems can make it difficult to identify and detect suspicious activities or errors in the system.
To ensure data is kept secure, businesses must have strong measures in place to protect against unauthorized access, encryption, and regular backups of data.
Furthermore, companies must train their staff on the proper use of the system. It helps staff to know how to protect confidential information from being accessed or misused by unauthorized personnel.
Businesses may also need an experienced IT team to monitor and maintain the system to keep up with any changes or updates for optimal performance.
Accounting automation has come a long way in the past few decades. It is likely to continue to advance in the future. As technology continues to evolve, more businesses will likely begin taking advantage of automation in their accounting processes. So, businesses should be aware of the potential challenges and prepare to stay competitive.
Author bio: Kassidy Li is a Certified Public Accountant and online entrepreneur who is passionate about helping people to solve problems and grow wealth with accounting knowledge and technology. She has 10+ accounting experience in small to large-scared corporations and expertise in financial accounting, management accounting, budgeting, and payroll.
Three ways data can help financial organisations thrive in today’s economy
By Rinesh Patel, Global Head of Financial Services, Snowflake
Financial organisations are caught in the middle of an ever-evolving landscape caused, in part, by emergent fintechs, shifting consumer expectations and increased regulatory change. Businesses are therefore turning to their data, re-imagining how they collect, process and analyse it, to drive growth and opportunity.
Despite this intention though, firms can often find themselves overwhelmed with the amount of data at their fingertips. Data tends to reside in individual departments that have no secure, efficient way of sharing it with other teams, creating silos of information. When teams need to collaborate, organisations are faced with additional costs and complexities in the movement of that data. The current infrastructure used by many financial institutions is not able to support the changing requirements of the industry, where data is the lifeblood.
Firms looking to harness their data should leave behind their outdated legacy architecture and implement an enterprise data strategy with a cloud-native platform. They can reposition themselves to accelerate time to market and value, with differentiated products and improved client offerings to gain a critical competitive advantage. Here are three ways that financial services are using better technology and enhanced data management to add business value.
Adhering to regulatory requirements
The volume of global regulations and reporting obligations has risen exponentially in the past decade, creating greater complexity and security challenges for firms capturing and processing data. Many of these regulations were taken by supervisors to ensure financial stability after the financial crisis of 2008. Regulators have greater expectations of firms with the aim of risk mitigation and transparency. With advanced technologies facilitating data capture, storage and analysis now available, supervisory bodies are also keen in part, to ask for additional disclosures because it’s now possible to demand more documentation and seek greater transparency.
The landscape of differing interpretations, overlapping regulatory requirements across asset classes and geographies and strict, even unrealistic deadlines for implementation have forced customers to take tactical quick-fix solutions, elevating operational risk and the chance of regulatory fines. Compliance departments have therefore been spending years building reporting processes, managing inconsistent data sets, maintaining ageing data stores and importantly overseeing differing levels of governance, adding more cost and complexity to the task at hand. For a large multi-segment global bank or asset manager this fragmented and manual approach to data management and analysis is not sustainable given the scale of processes and multi-geographic considerations that they have to comply with.
As regulators continue to push the long-term structural change agenda, financial services must now ready themselves to meet more robust reporting requirements to comply with the ever-changing regulatory landscape. The objective is to simplify and better manage data across teams with the governance and security provided by technological capabilities now offered through modern cloud capabilities to drive needed reporting. This will allow firms to replace old and inconsistent data with a centralised data architecture, providing a single source of truth. The time and cost reduction from data sourcing, ingestion, and the normalisation of data for analysis, can shrink to significantly streamline reporting processes.
Customer 360 experience
Consumers provide financial institutions with a vast amount of information, ranging from their banking habits to their behavioural preferences. Financial organisations have traditionally been slow to tap into the totality of this information to provide a better experience for customers.
The quest to provide greater visibility and a 360-degree view of customer behaviour is at the core of financial services organisations’ priorities. Customers want smooth, easy digital experiences that can speak to their desire for ease of use and convenience. This is seen in the ways virtual banking consumers have opted for technologies that are simple to interact with, self-directed and frictionless when it comes to carrying out digital transactions. New regulations, such as PSD2 and rules around open banking have also primed customers to expect more.
The challenge for legacy institutions is to bring the ease and usability of digital-first platforms with the sophistication of a major, global provider. Tapping into the full spectrum of data created by consumers is central to a successful transition.
Wealth advisory, investment management professionals are increasingly looking at data capabilities to support ongoing relationship management with their clients. Using data to understand customers in this way helps banks to successfully move customers up the wealth value chain. Wealth management organisations can digitise the investment process – from finding customers to managing accounts, and offering bespoke plans. Effective use of data in this sector can free up time for advisors, helping to retain key customers and charge higher commission levels thanks to a new level of personalised service.
Developing an effective ESG strategy
Environmental, social and corporate governance (ESG) considerations have grown in significance with increasing stakeholder pressures, driving a response by firms to prioritise their sustainability agenda. To understand, evaluate the problem and take action, firms need access to technology providing holistic ESG data capabilities and solutions, with performance and scale.
Financial firms are amassing large data sets from the public sector, including government reports, scientific bodies and private sector reports, to understand and address the climate challenge. Businesses are moving with urgency to acquire robust data sets, to meet ESG criteria and sustainability metrics needed to evaluate impact and make progress against their own commitments. There are several pervasive business use cases for teams experiencing ESG data challenges, including portfolio construction, financial planning and regulatory reporting that will require an effective ESG data management strategy.
Ever present challenges in the ingestion, standardisation, and sharing of ESG data will be at the forefront of every organisation – as they process the magnitude of the challenge and transform their operations to address the issue. With cloud-native solutions, firms can use ready-to-use query data across established marketplace data sets. They can then share that data across teams in a secure, governed way – with greater speed to market. Organisations can meet the need for scalable analytics, and access a data ecosystem to build their own proprietary ESG applications for different user and workflow requirements.
A business fit for the future
With data cloud solutions, businesses can effectively analyse the vast amounts of data available to them, equipping them to meet the ever-changing financial landscape. Leaving behind legacy systems will open up a multitude of opportunities and benefits that will drive business growth. This includes developing a 360 view of the customer, improved data governance and the opportunity to use data to support an effective ESG strategy. Without the ability to harness data through the cloud, companies will get left behind the competition and struggle to meet the standards that modern consumers expect.
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