Perfecting Finance Operations through Recs and Retrieval

Perfecting Finance Operations through Recs and Retrieval

This article tackles three issues facing CFOs and other finance leaders.

  • How spreadsheets are collapsing under the weight of big data and how can we shore them up
  • Managing the impact on finance operations in the age of data source proliferation
  • How manual processes are limiting capabilities and what to do about it

How spreadsheets are collapsing under the weight of big data and how can we shore them up

From the early periods of personal computing, spreadsheets came into foray and became a mainstay for most business operations. Finance teams in particularly rely on spreadsheets to manage day-to-day operations as well as long term tasks such as auditing. The spreadsheets has served these purposes well. However, with the advent of phone-based payments and increase in online commerce, finance departments are getting a deluge of data from multiple sources like payment gateways, banks, aggregators, order systems, acquirers, insurers, treasury, etc.

This spreadsheets-based manual approach which worked previously can barely pull the weight of managing large operations. And even if they do, finance teams end up with a large amount of data scattered across an ever-increasing number of siloed spreadsheets. Auditing these spreadsheets becomes an extremely challenging task. To make matters worse, finding a particular transaction with up-to-date information is almost impossible.

Our primary research has indicated this needle-in-the-haystack problem is faced by companies in multiple industries like payment processing, marketplaces, e-commerce, direct-to-consumer, cloud kitchens, and neo banking. We also found that customers are looking for solutions to centralise and automate finance operations while improving other elements like tracking, monitoring, auditing, reporting, and searchability. We talked to finance controllers of roughly 65 companies spanning multiple industries and geographies.

An independent study conducted by Deloitte found that a significant number of finance departments are struggling to manage large amounts of data. However, very few organisations are doing the heavy lifting to automate operations and be future ready.

Managing the impact on finance operations in the age of data source proliferation

In today’s digital age, data has become the backbone of businesses. Companies use data to gain insights, make decisions, and drive growth. However, with the increasing volume and complexity of data, reconciling it has become a significant challenge for many organizations. Let’s see what these challenges are and what a possible solution might be.

Data silos and fragmentation

One of the biggest challenges in reconciling data from multiple sources is the existence of data silos. Data silos are collections of data that are isolated from the rest of an organization’s data, often due to the use of different software systems or data management practices. As a result, data may be fragmented across various silos, making it difficult to consolidate and reconcile.

Lack of data standardization

Multiple data sources, like internal financial records, bank statements, third-party payment processors, and point-of-sale systems, come into play while reconciling data. One of the primary challenges in reconciling data from these many multiple sources is the lack of standardisation.

When data is not standardized, it can have different formats, units, definitions, or labels, which can create confusion, errors, or mismatches in the reconciliation process. According to a report by Deloitte, 1 in 6 reconciliations are noted with some errors or exceptions.

Data Integration Challenges

It’s important for an organization to ensure the accuracy and integrity of financial data, which often involves reconciling data from various sources, such as different subsidiaries, departments, or systems. However, reconciling data from various sources can lead to data integrity issues due to differences in data formats, currencies, accounting practices, and other factors. These issues can make it challenging to accurately consolidate financial data and can potentially result in inaccurate financial reporting, which can have significant consequences for the organisation.

Data Security Challenges

Data from multiple sources may contain sensitive or confidential information that requires protection. Ensuring data security can be challenging when dealing with data from external sources, which may not have the same security protocols as internal sources.

The case of Kensington and Chelsea Council being fined £120,000 highlights the potential consequences of failing to ensure the security of data from different sources. The pivot table error in this instance led to the exposure of hundreds of property owners’ data, which not only put the individuals affected at risk but also damaged the council’s reputation.

The Role of advanced technologies in Reconciliation

Advanced technologies such as artificial intelligence (AI) and machine learning (ML) can play a crucial role in improving the reconciliation process.

A medical device manufacturer faced challenges with inconsistent processes, sources, formats, and structures across different business units. To address this, they integrated AI and automation, resulting in improved work allocation, reduced transactional tasks, and increased time spent on strategic initiatives. The integration also improved balance sheet integrity, leading to a $225 million reduction in inter-company balances within two years.

How manual processes are limiting capabilities and what to do about it

It goes without saying that the finance department plays a significant strategic role for any company with a certain scale. Founders and leadership teams depend on unique insights, or in the very least, take of the finance team on pertinent issues and challenges. However, the research indicates that a significant amount of time (almost half of a work week) of finance teams goes in performing rote manual work of transactional nature. This was reported by finance organizations of 832 companies researched by APQC. A similar survey done Census wide also indicated a similar trend.

We also landed on similar findings gathered from the primary research we conducted. With the increase in payment modes and the associated infrastructure, the scale of manual work of finance teams has been increasing. In fact, the volume and complexity around payments data have exploded leading to the failure of manual approaches and tools.

Some of the rote steps a finance teams carries out on daily basis include:

  1. Data Gathering
  2. Data Cleaning & Formatting
  3. Data Analysis
  4. Reporting

A report by McKinsey indicates four areas where technology can add significant benefits:

  1. Automation to improve and streamline processes in finance operations
  2. Data visualization to provide real-time financial information to improve organizational performance
  3. Advanced analytics for finance operations to accelerate decision making
  4. Insights into overall business operations to uncover hidden growth opportunities

Kosh’s platform helps businesses in completely automating finance operations. Kosh removes the challenges around data gathering, cleaning, and processing while completely automating analysing reporting. Our customers are able to remove manual efforts and improve efficiency at least by 98%. With the help of Kosh, our customers are able to reduce time from a couple of weeks to less than 5 minutes.


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