Article | August 9, 2021
The financial industry has been going through digital transformation for years. Digital technologies have helped to automate manual and tedious tasks like processing and reporting of historical data to forecasting and financial predictive analytics.
The financial services industry owes its success to data. Data is constantly evolving in the form of market trends, client investment, customer service, campaigns. Data gives a boost to banking strategies. As reported by Accenture in a recent survey, 78 percent of banks have made the shift to using data for operations; however, only seven percent of them have extended to using predictive analytics in finance.
Predictive analytics in finance has had a slow but steady start. It is an area of growing interest for banks and other institutions as new newer technologies launch in the market. To complete your company’s digital transformation, data analytics in finance will make a difference in that process.
To be successful, organizations must have the ability to adapt to changes.
Having predictive analytics on your side, your organization can deal with ever-changing circumstances with less to no difficulty.
Understanding Predictive Analytics: What is it?
Predictive analytics is a process of interpreting data to measure any possible future outcomes. It is carried out with the help of statistical modeling, historical data sets, and machine learning. The collected historical data is fed into an algorithm that recognizes patterns and forecast trends and possible future behavior from days to years in advance.
Analyzing historical data and predicting the future has been an old practice in the finance sector. Banks and financial institutions have been evaluating past events or historical data for a long time now.
Making precise forecasts in trends and analyzing data becomes easier due to predictive analytics.
There is a wider scope to predictive efforts with more speed and accuracy and apply them throughout strategic and tactical business practice areas.
Predictive Analytics in the Financial Sector: What are the Benefits?
Many organizations are ready to accept the positive applications of predictive analytics but remain skeptical about the return on investment.
It is worth understanding the potential of predictive analytics to any business big or small. It doesn’t matter if you are not in the banking sector to benefit from taking a peek into the future of financial performance.
Any finance and accounting department can take advantage of advanced predictive analytics for the following reasons:
The technology keeps a regular track of the consistency between expectations and reality to warn you about possible gaps.
Analytics accurately helps you identify any possible threats to your business and warns you.
Enhanced User Experience
Predictive analytics guides you to recognize the strengths of your business and lets you know how to maximize customer satisfaction.
Analyzed Decision Making
You can understand your customers better with predictive analytics. With this information, you can correctly match your customers with the product in a better way.
Importance of Predictive Analytics
Most successful banking and financial institutions depend on predictive analytics because it simplifies and integrates data to increase profits for companies. Predictive analytics can improve different finance processes.
But the importance of analytics goes beyond just banking services and actually goes into a better quality of customer service. Better customer service is only possible because of the advanced technology that shares customer feedback and preferences throughout the organization, in turn giving relevant information to every employee to make necessary product enhancements.
To understand the importance of predictive analytics, below are some of its use cases:
Predictive analytics in financial institutions and banking give you a complete profile of your customer base. It is impossible to contact every customer and interview them about their likes, needs and wants. This is where big data analytics in finance comes into play. It gives you the whole information about your customers regardless of the services they subscribe.
Customers usually don’t have the same needs throughout their lives. As they grow older and have families, their financial needs change accordingly. For instance, a young person considering getting married will always try and save monetarily to buy a house, life insurance, college funds, whereas an older couple will save that money for their retirement.
Apart from enabling different financial services, predictive analytics empowers you to serve individual customers with ease. Let’s take an example. When a customer applies for a loan, predictive financial services can help you analyze if the customer can repay the loan.
Predictive analytics also helps offer alternative services like secured loans to customers who may not qualify for the originally applied services.
Online Banking Made Better
Consumer interest fluctuates in spikes. Predictive analytics informs managers enough in advance so they can set up online infrastructures in those areas. Predictive analytics has made it easier to identify a possible customer base. For example, it can provide metrics to the marketing teams. In turn, the marketing teams can target the customers with ads for probable mortgage loans or business loans in hopes of converting them into their customers.
Data analytics in finance also helps in preventing and detecting fraud and abuse. Although detecting fraud doesn’t necessarily fall under predictive analytics, it can inform the IT department about potential scammers and which online services must be protected.
Foreseeing Market Variations
Predictive analytics can predict market variations and changes. By combining internal and external data, your organization can predict revenue growth in particular market sectors.
For nascent or growing companies, predicting market changes is an important ability. Profitable companies should also be reviewed through predictive analytics to generate demand projections owing to the uncertainties caused by the Covid-19 pandemic. Your return on investment can grow or reduce even with the minutest changes to the growth plans that would seriously impact investor confidence in the future.
Predictive analytics also help to establish which marketing campaigns are working and which strategies need to change.
Predictive Analytics and the Future: What Next?
Technological improvements have allowed predictive analytics in finance to improve and change constantly. Any organization can use customized data solutions to meet your customers’ needs and reach new ones efficiently. Your organization can use predictive analytics to move your business and products ahead and understand how the market will thrive, giving you the much needed heads up you would need to change your strategies and tactics.
Frequently Asked Questions
Is predictive analytics is the future of finance?
Predictive analytics is called the ‘future of financial software,’ which means it can provide accurate planning and cost-effectiveness.
How can analytics be used in finance?
Analytics helps in predicting revenue, improve supply chains, identify trouble spots, understand where the company is bleeding money, and fraud detection.
How do predictive analytics benefit financial institutions?
Predictive analytics can help financial institutions and customers detect fraud, financial management, predicting markets, improving products, better user experience, etc.
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Article | August 9, 2021
Data has settled into regular business practices. Executives in every industry are looking for ways to optimize processes through the implementation of data. Doing business without analytics is just shooting yourself in the foot.
Yet, global business efforts to embrace data-transformation haven't had resounding success. There are many reasons for the challenging course, however, people and process management has been cited as the common thread.
A combination of people touting data as the “new oil” and everyone scrambling to obtain business intelligence has led to information being considered an end in itself. While the idea of becoming a data-driven organization is extremely beneficial, the execution is often lacking. In some areas of business, action over strategy can bring tremendous results.
However, in data governance such an approach often results in a hectic period of implementations, new processes, and uncoordinated decision-making. What I propose is to proceed with a good strategy and sound data governance principles in mind.
Auditing data for quality
Within a data governance framework, information turns into an asset. Proper data governance is essentially informational accounting. There are numerous rules, regulations, and guidelines to make governance ensure quality.
While boiling down the process into one concept would be reductionist, by far the most important topic in all information management and governance is data quality. Data quality can be loosely defined as the degree to which data is accurate, complete, timely, consistent, adherent to rules and requirements, and relevant.
Generally, knowledge workers (i.e. those who are heavily involved in data) have an intuitive grasp of when data quality is lacking. However, pinpointing the problem should be the goal. Only if the root cause, which is generally behavioral or process-based rather than technical, of the issue is discovered can the problem be resolved.
Lack of consistent data quality assurance leads to the same result with varying degrees of terribleness - decision making based on inaccurate information. For example, mismanaging company inventory is most often due to lack of data quality. Absence of data governance is all cost and no benefit. In the coming years, the threat of a lack of quality assurance will only increase as more businesses try to take advantage of data of any kind.
Luckily, data governance is becoming a more well-known phenomenon. According to a survey we conducted with Censuswide, nearly 50% of companies in the financial sector have put data quality assurement as part of their overall data strategy for the coming year.
Data governance prerequisites
Information management used to be thought of as an enterprise-level practice. While that still rings true in many cases today, overall data load within companies has significantly risen in the past few years. With the proliferation of data-as-a-service companies and overall improvement in information acquisition, medium-size enterprises can now derive beneficial results from implementing data governance if they are within a data-heavy field.
However, data governance programs will differ according to several factors. Each of these will influence the complexity of the strategy:
Business model - the type of organization, its hierarchy, industry, and daily activities.
Content - the volume, type (e.g. internal and external data, general information, documents, etc.) and location of content being governed.
Federation - the extent and intensity of governance.
Smaller businesses will barely have to think about the business model as they will usually have only one. Multinational corporations, on other hand, might have several branches and arms of action, necessitating different data governance strategies for each.
However, the hardest prerequisite for data governance is proving its efficacy beforehand. Since the process itself deals with abstract concepts (e.g. data as an asset, procedural efficiency), often only platitudes of “improved performance” and “reduced operating costs” will be available as arguments. Regardless of the distinct data governance strategy implemented, the effects become visible much later down the line. Even then, for people who have an aversion to data, the effects might be nearly invisible.
Therefore, while improved business performance and efficiency is a direct result of proper data governance, making the case for implementing such a strategy is easiest through risk reduction. Proper management of data results in easier compliance with laws and regulations, reduced data breach risk, and better decision making due to more streamlined access to information.
“Why even bother?”
Data governance is difficult, messy, and, sometimes, brutal. After all, most bad data is created out of human behavior, not technical error. That means telling people they’re doing something wrong (through habit or semi-intentional action). Proving someone wrong, at times repeatedly, is bound to ruffle some feathers.
Going to a social war for data might seem like overkill. However, proper data governance prevents numerous invisible costs and opens up avenues for growth. Without it, there’s an increased likelihood of:
Costs associated with data. Lack of consistent quality control can lead to the derivation of unrealistic conclusions. Noticing these has costs as retracing steps and fixing the root cause takes a considerable amount of time. Not noticing these can cause invisible financial sinks.
Costs associated with opportunity. All data can deliver insight. However, messy, inaccurate, or low-quality data has its potential significantly reduced. Some insights may simply be invisible if a business can’t keep up with quality.
As data governance is associated with an improvement in nearly all aspects of the organization, its importance cannot be overstated. However, getting everyone on board and keeping them there throughout the implementation will be painful. Delivering carefully crafted cost-benefit and risk analyses of such a project will be the initial step in nearly all cases.
Luckily, an end goal to all data governance programs is to disappear. As long as the required practices and behaviors remain, data quality can be maintained. Eventually, no one will even notice they’re doing something they may have considered “out of the ordinary” previously.
BIG DATA MANAGEMENT
Article | August 9, 2021
As the organizations go digital the amount of data generated whether in-house or from outside is humongous. In fact, this data keeps increasing with every tick of the clock.
There is no doubt about the fact that most of this data can be junk, however, at the same time this is also the data set from where an organization can get a whole lot of insight about itself.
It is a given that organizations that don’t use this generated data to build value to their organization are prone to speed up their obsolescence or might be at the edge of losing the competitive edge in the market.
Interestingly it is not just the larger firms that can harness this data and analytics to improve their overall performance while achieving operational excellence. Even the small size private equity firms can also leverage this data to create value and develop competitive edge. Thus private equity firms can achieve a high return on an initial investment that is low.
Private Equity industry is skeptical about using data and analytics citing the reason that it is meant for larger firms or the firms that have deep pockets, which can afford the revamping cost or can replace their technology infrastructure. While there are few private equity investment professionals who may want to use this advanced data and analytics but are not able to do so for the lack of required knowledge.
US Private Equity Firms are trying to understand the importance of advanced data and analytics and are thus seeking professionals with the expertise in dealing with data and advanced analytics. For private equity firms it is imperative to comprehend that data and analytics’ ability is to select the various use cases, which will offer the huge promise for creating value. Top Private Equity firms all over the world can utilize those use cases and create quick wins, which will in turn build momentum for wider transformation of businesses.
Pinpointing the right use cases needs strategic thinking by private equity investment professionals, as they work on filling the relevant gaps or even address vulnerabilities. Private Equity professionals most of the time are also found thinking operationally to recognize where can they find the available data.
Top private equity firms in the US have to realize that the insights which Big data and advanced analytics offer can result in an incredible opportunity for the growth of private equity industry. As Private Equity firms realize the potential and the power of big data and analytics they will understand the invaluableness of the insights offered by big data and analytics.
Private Equity firms can use the analytics insights to study any target organization including its competitive position in the market and plan their next move that may include aggressive bidding for organizations that have shown promise for growth or leaving the organization that is stuffed with loads of underlying issues.
But for all these and also to build careers in private equity it is important to have reputed qualification as well. A qualified private equity investment professional will be able to devise information-backed strategies in no time at all.
In addition, with Big Data and analytics in place, private equity firms can let go of numerous tasks that are done manually and let the technology do the dirty work. There have been various studies that show how big data and analytics can help a private Equity firm.
THEORY AND STRATEGIES
Article | August 9, 2021
We discursive creatures are construed within a meaningful, bounded communicative environment, namely context(s) and not in a vacuum.
Context(s) co-occur in different scenarios, that is, in mundane talk as well as in academic discourse where the goal of natural language communication is mutual intelligibility, hence the negotiation of meaning. Discursive research focuses on the context-sensitive use of the linguistic code and its social practice in particular settings, such as medical talk, courtroom interactions, financial/economic and political discourse which may restrict its validity when ascribing to a theoretical framework and its propositions regarding its application. This is also reflected in the case of artificial intelligence approaches to context(s) such as the development of context-sensitive parsers, context-sensitive translation machines and context-sensitive information systems where the validity of an argument and its propositions is at stake.
Context is at the heart of pragmatics or even better said context is the anchor of any pragmatic theory: sociopragmatics, discourse analysis and ethnomethodological conversation analysis. Academic disciplines, such as linguistics, philosophy, anthropology, psychology and literary theory have also studied various aspects of the context phenomena. Yet, the concept of context has remained fuzzy or is generally undefined. It seems that the denotation of the word [context] has become murkier as its uses have been extended in many directions.
Context or/ and contexts? Now in order to be “felicitous” integrated into the pragmatic construct, the definition of context needs some delimitations. Depending on the frame of research, context is delimitated to the global surroundings of the phenomenon to be investigated, for instance if its surrounding is of extra-linguistic nature it is called the socio-cultural context, if it comprises features of a speech situation, it is called the linguistic context and if it refers to the cognitive material, that is a mental representation, it is called the cognitive context. Context is a transcendental notion which plays a key role in interpretation.
Language is no longer considered as decontextualized sentences. Instead language is seen as embedded in larger activities, through which they become meaningful. In a dynamic outlook on communication, the acts of speaking (which generates a form discourse, for instance, conversational discourse, lecture or speech) and interpreting build contexts and at the same time constrain the building of such contexts. In Heritage’s terminology, “the production of talk is doubly contextual” (Heritage 1984: 242). An utterance relies upon the existing context for its production and interpretation, and it is, in its own right, an event that shapes a new context for the action that will follow. A linguistic context can be decontextualized at a local level, and it can be recontextualized at a global level. There is intra-discursive recontextualization anchored to local decontextualization, and there is interdiscursive recontextualization anchored to global recontextualization. “A given context not only 'legislates' the interpretation of indexical elements; indexical elements can also mold the background of the context” (Ochs, 1990). In the case of recontextualization, in a particular scenario, it is valid to ask what do you mean or how do you mean. Making a reference to context and a reference to meaning helps to clarify when there is a controversy about the communicative status and at the same time provides a frame for the recontextualization.
A linguistic context is intrinsically linked to a social context and a subcategory of the latter, the socio-cultural context. The social context can be considered as unmarked, hence a default context, whereas a socio-cultural context can be conceived as a marked type of context in which specific variables are interpreted in a particular mode. Culture provides us, the participants, with a filter mechanism which allows us to interpret a social context in accordance with particular socio-cultural context constraints and requirements. Besides, socially constitutive qualities of context are unavoidable since each interaction updates the existing context and prepares new ground for subsequent interaction.
Now, how these aforementioned conceptualizations and views are reflected in NLP? Most of the research work has focused in the linguistic context, that is, in the word level surroundings and the lexical meaning. An approach to producing sense embeddings for the lexical meanings within a lexical knowledge base which lie in a space that is comparable to that of contextualized word vectors.
Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information. The task of associating a word in context with the most suitable meaning from a predefined sense inventory is better known as Word Sense Disambiguation (Navigli, 2009). Linguistically speaking, “context encompasses the total linguistic and non-linguistic background of a text” (Crystal, 1991). Notice that the nature of context(s) is clearly crucial when reconstructing the meaning of a text. Therefore, “meaning-in-context should be regarded as a probabilistic weighting, of the list of potential meanings available to the user of the language.” The so-called disambiguating role of context should be taken with a pinch of salt.
The main reason for language models such as BERT (Devlin et al., 2019), RoBERTA (Liu et al., 2019) and SBERT (Reimers, 2019) proved to be beneficial in most NLP task is that contextualized embeddings of words encode the semantics defined by their input context. In the same vein, a novel method for contextualized sense representations has recently been employed: SensEmBERT (Scarlini et al., 2020) which computes sense representations that can be applied directly to disambiguation.
Still, there is a long way to go regarding context(s) research. The linguistic context is just one of the necessary conditions for sentence embeddedness in “a” context. For interpretation to take place, well-formed sentences and well-formed constructions, that is, linguistic strings which must be grammatical but may be constrained by cognitive sentence-processability and pragmatic relevance, particular linguistic-context and social-context configurations, which make their production and interpretation meaningful, will be needed.