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Driven by developments in artificial intelligence and big data, the whole financial industry is undergoing a fundamental change that will become even more pronounced in the coming years. The associated changes entail many opportunities, but also numerous risks. It is already foreseeable that there will be both winners and losers, especially since the degree of maturity of the use of artificial intelligence in banks is very different.
But how can a bank actually notice that it is being left behind from current developments? Here are some scenarios that those banks may experience. The list is subjective and not complete. In addition, there is a risk that countermeasures are no longer possible if the points mentioned below actually materialize.

Increasingly stronger competitors.
Banks can significantly increase their efficiency through the use of AI methods, such as automated text analysis.They can pass this on directly to the customers and offer significantly better conditions. Through intelligent customer analysis, they can also offer tailor-made solutions and react quickly to changing customer requirements.
The other banks will experience that the competition is “unexpectedly” strengthening even in their traditional markets, offering ever-better conditions, exacerbating price pressure and always seeming a bit faster. Despite good economic conditions, this will lead to a loss of profits.

More critical customers.
Customers are becoming increasingly demanding and critical as a result of the aforementioned tailor-made solutions from competitors. What is initially considered an additional feature will become a matter of course over time. As a result, banks will face more and more demanding customers who, in the case of the negative scenario considered, can no longer satisfy them.

Worse risk-return ratio.
Improved early detection procedures – such as text messaging or geographic information – will allow high-credit-mature banks to tailor the risk premium to their actual credit rating.
As a result, customers with poor credit ratings will increasingly migrate to the “suspended” banks. These will face the decision to take disproportionately high risks or operate such conservative lending that little more profits are left.

More frauds.
KI procedures in the area of compliance enable the detection of money laundering, for example, but also internal cases of fraud, e.g. in the retail sector, even in advance. Consequently, there will be a tendency for “fraudulent” individuals to dodge on low-maturity banks. These will correspondingly have more to do with compliance-related issues.

Negative press.
Among other things, AI processes enable the targeted, real-time tracking of reputation and possible reputational risks. Banks that have implemented such procedures can detect such risks at an early stage and take appropriate countermeasures. Reputation-damaging projects tend to be more likely to be carried out by banks with low AI maturity. Accordingly, the focus of journalists – even with regard to the compliance cases mentioned – will shift to these banks.

Thus, it appears that in the coming years, even in the banking industry, there will be extreme competition for the best AI procedures and a “war for talent”. https://goo.gl/qd215q #DataScience #Cloud

An emerging trend in the private equity space is an enhanced focus on data science.

This focus has historically been more on the operations side (post-acquisition) where data scientists have been leveraged to help companies improve performance in many key areas including marketing, business intelligence, financial analysis, and human resources. The advantage of focusing data scientists on the operations side is rather obvious: after an acquisition has occurred, the data challenges and opportunities of the portfolio company are more accessible.

More recently, some target companies have been opening up their books a bit more at the due diligence stage. This allows private equity firms to dig more deeply into the key revenue and profitability assumptions. Key analytics at this stage can include understanding not only the detailed drivers of profitability but also customer segments, factors influencing operations efficiency and direct competitor comparisons. Exploratory insights from mining databases including public data, social media and data vendors can be linked with company data to see if external trends are similar to internal trends. This may help better define any unique value propositions that company has or market segments they potentially can explore. For businesses with physical locations (retail, restaurant, etc.), data scientists can explore the assumptions regarding future site selection, projected number of sites growth, cannibalization, etc. For customers with direct customer contacts, data scientists can help understand the customer value

Leveraged buyout models (often Excel-based financial calculations) tend to have a rather standard structure. The formulas are well understood by financial analysts so many companies are using the same (or very similar) financial calculations. The variation across companies is usually driven by the key input assumptions including the revenue and income growth assumptions.

A key distinguishing factor for private equity firms is how well they understand the key inputs to those leveraged buyout models. Detailed data analytics at the due diligence stage can give them an advantage in better understanding the assumptions and limitations in the projected revenue and projected earnings…resulting in more accurate projections of the target companies value. #privateequity #datascience #data #analytics https://goo.gl/Z3ecCs #DataScience #Cloud

Hardly a day goes by without hearing the importance of data: “data is the new gold!”, “data is the new black gold!”… As you can see, knowing how to properly exploit data at your disposal has become critical for almost all sectors.

“…If the first to recruit are from digital services, banking and insurance, the health sector is not left with the desire to see the emergence of a predictive, preventive, personalized and participatory medicine. Eventually the phenomenon will extend to the entire industrial sector: transport, energy, nuclear…” 

Highway asset management is no exception in digital transformation, it is no longer exclusively for people who love asphalt and concrete, it can be a fulfilling career for creative types as well.

The concept of digital transformation in highway asset management is not new. Transportation authorities around the world has been making the transition from manual to fully automated network highway condition data collection to assess overall health of highway network and for asset management purposes.

But the data issues have been greatly amplified in recent years as the technologies become increasingly sophisticated.  Transportation authorities need to keep pace with the increasing data volumes and complexity in data types from the transportation Big Data sources“generated by sensors and data collection points from passenger counting systems, vehicle location systems, ticketing and fare collection systems, and scheduling and asset management systems.” To continue support pavement management decisions, it therefore becomes imperative to be able to quickly visualize and analyze these huge amounts of pavement data.

What does this mean for transportation authorities?

When the traditional engineering meets the digital transformation, what does this mean to the transportation authorities? It means that the authorities are able to keep the roads healthier and safer by knowing more about our road condition, making better decision on operation investment, and developing better plans for road maintenance, as long as we know how to use this data correctly.

Today, no one can afford to leave the data solely in the hands of a few experts, formerly reserved for users who master the technical tools. Data analysis and visualization solutions must now go through all hands to enable a 360-degree view of our activities and a better responsiveness to all aspects of the business.

Unsurprisingly, this shift also sharply raises expectations of the data ability to deliver detailed, accurate reports to support new business models and investment strategies.

What are the challenges?

1. Obsolete tools

In parallel with the digital transformation, we can still see that many organizations do not have the means at their disposal to allow everyone to access analytics to drive their business.

2. A problem of evolution

Many organizations are still reluctant to change the historical tools that were implemented many years ago. However, these tools no longer allow users to respond to requests. Often, managers who cannot use them are dependent on a third party to build their reports and access key indicators. It is a waste of time that affects the responsiveness of the organization.

3. Unfulfilled promises

Others have chosen new generation of Business Intelligence (BI) tools that promise a better user experience. This is a good idea in theory, however, in practice, most solutions are inadequate and do not allow a user without technical knowledge to create a dashboard on its activities on demand.

4. Organizations are still “beginners”

Some organizations are not yet properly equipped and continue to use tools like Excel in most of their departments. These tools can be very effective at first. But they become heavy and time-consuming when you start wanting to do analyzes on different data sources or when you want to have a global and shared view of the organization’s activities.

Why data science and data visualization is Important?

Following are the top 5 reasons to take advantage of data visualization for pavement management:

1. Benefit from a new, more efficient way of transmitting and assimilating information

Generally, asset management information compiled by the technical teams includes static charts and graphs. Transforming this approach to web-based data visualization is more likely to find new ways to interpret data. Both the technical and management teams will find the information they need more easily and are more productive.

2. Find links and trends between different highway data

By facilitating correlations, data visualization allows the asset management team to identify problem sources faster and act faster to resolve them. The data can also be compared quickly and easily.

3. Respond more quickly to highway condition deterioration

Highway condition indicators can be used to discover changes on highway condition. Being able to visualize these indicators are critical for investment decision-making on highway assets as it allows organizations to capture highway condition deterioration over time.

4. Interact directly with the data

Data visualization tools not only allow you to see the highway asset data, but also to manipulate and interact with it. The most effective actions can therefore be put forward based on analytical models developed through visualization and predictive analytics.

5. Tell compelling stories

Data visualization can engage management teams towards the highway data results in greater performance. The way the pavement data accessible to all will enable both technical and management team to tell a story through the data. https://goo.gl/NwGaNu #DataScience #Cloud