ECB publishes Consolidated Banking Data for end-December 2017

News

Giu 22 2018

The European Central Bank (ECB) published the consolidated banking data at the end of December. The annual CBD statistics cover relevant information required for the analysis of the EU banking sector, covering a broader set of data than the quarterly release. The end-December 2017 data refer to 377 banking groups and 2,884 stand-alone credit institutions operating in the EU (including foreign subsidiaries and branches), covering nearly 100% of the EU banking sector balance sheet. This dataset includes an extensive range of indicators on profitability and efficiency, balance sheets, liquidity and funding, asset quality, asset encumbrance, capital adequacy and solvency.

Source: European Central Bank

The amount of total assets of financial institutions keeps the downward trend of the latest year, both in the Euro Area (EA) and in the European Union (EU) as a whole.

Source: European Central Bank

Particular interest is devoted to the stock of non-performing loans (NPL) within financial institutions balance accounts. The amount of NPL (as % of total assets) continued to decrease, from 6.49% of total assets at the end of 2016 to 4.83% of total assets for the Euro Area and from 5.29% of total assets to 4.06% of total assets in the European Union.

Source: Editor’s computation on ECB data

By examining the growth rate of each of these money stocks separately, we notice that the assets of financial institutions followed almost the main pattern in the EA and in the EU, decreasing by -2.89% and -3.30% on aggregate in the last year, respectively.

The NPL instead decreased much more rapidly in the Euro Area, especially in the last quarter of 2017; on aggregate, NPL decreased by nearly 28% in the EA compared to 25% in the EU.

As a consequence, the difference between the growth rate of banks assets and the growth rate of NPL is larger in the EA than in the EU. This indicates that the Euro Area is able to recover bank’s profitability faster despite the downward trend of financial institutions assets.

Banca d’Italia: aggiornamento regole di compilazione del bilancio degli intermediari IFRS non bancari

News

Giu 22 2018

La Banca d’Italia ha sottoposto in consultazione le bozze del sesto aggiornamento alle modifiche del provvedimento “Il bilancio degli intermediari IFRS (International Financial Reporting Standards) diversi dagli intermediari bancari” (2017) all’interno della circolare “Il bilancio bancario: schemi e regole di compilazione” (2005).

Gli interventi di modifica delle disposizioni di bilancio recepiscono le novità introdotte dal principio contabile internazionale IFRS 16 “Leasing”, omologato con il Regolamento (UE) 2017/1986 del 31 ottobre 2017, che sostituirà il vigente principio contabile IAS 17 “Leasing” ai fini del trattamento in bilancio del leasing a partire dal 1° gennaio 2019.

L’adozione dell’IFRS 16 ha comportato la modifica di altri principi contabili internazionali, tra cui lo IAS 40 in materia di investimenti immobiliari, al fine di garantire la coerenza complessiva del framework contabile. I principali aspetti di novità introdotti dall’IFRS 16 riguardano:

  1. l’ampliamento del perimetro di applicazione delle regole sul leasing. Il principio richiede infatti di identificare se un contratto è (oppure contiene) un leasing, basandosi sul concetto di controllo dell’utilizzo di un bene identificato per un periodo di tempo; di conseguenza possono rientrarvi anche i contratti di affitto o locazione, in precedenza non assimilati al leasing;
  2. l’introduzione di un unico modello di contabilizzazione dei contratti di leasing da parte del locatario, con la conseguente eliminazione della distinzione tra leasing operativo e leasing finanziario);
  3. la revisione della disclosure relativa ai contratti di leasing e al relativo trattamento contabile.

Il modello di contabilizzazione dei contratti di leasing da parte del locatore è rimasto invariato. Con l’occasione, è stato recepito l’emendamento del principio contabile internazionale IFRS 12 “Disclosure of Interests in Other Entities”, che chiarisce gli obblighi di disclosure per le partecipazioni riclassificate tra le attività possedute per la vendita ai sensi dell’IFRS 5.

Le circolari segnaletiche sono modificate per allinearle all’aggiornamento delle disposizioni di bilancio. È stata inoltre:

  1. integrata l’informativa sulla qualità del credito dei soggetti vigilati per consentire la piena riconciliazione con la segnalazione armonizzata a livello europeo delle attività deteriorate (FINREP);
  2. inserita una voce relativa alle operazioni di acquisto di crediti diversi da quelle effettuate nelle operazioni di factoring (analoga a quella già esistente sugli acquisti rientranti nell’ambito del factoring), contenente alcuni dettagli informativi sui crediti verso la P.A.

Aggiornamento delle disposizioni in materia di bilancio e di segnalazioni delle banche e degli intermediari IFRS diversi dagli intermediari (HTML)

Aggiornamento IFRS – Documento per la consultazione (PDF)

Talking AI: how social media affects algorithms
di Elisabetta Basilico

Articolo

Giu 22 2018
Talking AI: how social media affects algorithms di Elisabetta Basilico

Big Data is being increasingly used in many spheres of investment, and identifying sources of information which lend themselves to this practice has become a hot topic both in academia and the investment profession.

Social media is an obvious contender here and can be thought of as a database of society’s behaviour and a medium for capturing investor sentiment via Twitter and financial blogs, to name but a few.

As behavioural finance continues to challenge the notion of efficient markets, an interesting research question for the investment management profession is whether comments shared on social media are correlated to, or even predictive of, the state of the global economy and the future performance of stocks and markets.

Twittering into the future

One of the first papers on this topic, titled ‘Twitter Moods Predict the Stock Market’, was published in the Journal of Computational Science in 2011 by a trio of academics, who investigated the links between the daily content of 9.7 million tweets posted by 2.7 million users between March and December 2008 and the Dow Jones Industrial Average (DJIA).

They did so by using two tools to assess the mood of a tweet: OpinionFinder, a publicly-available software package to measure sentiment analysis, and GPOM, which is a little bit more sophisticated in that it measures six dimensions of mood instead of just positive or negative.

Their results did show significant correlation between one Twitter sentiment dimension and the direction of the DJIA. However, this study can be easily criticised because of the short length of the data series and a lack of out-of-sample testing.

Since the publication of the above study, other researchers have started investigating social media as a potential factor in predicting stock market returns.

For example, a team from Johns Hopkins University published a study in the Journal of Portfolio Management last year, calling social media the ‘sixth factor’ in an asset pricing model of stock returns.

They argued that social media is a distinct factor on top of the five advocated by famous academic duo, Eugene Fama and Kenneth French, who updated their three-factor model to a five-factor model (size, value, momentum, profitability and investment) in 2015.

The John Hopkins team researched sentiment-based content published on StockTwits, a social media platform that collects views on specific securities generated by the crowd, typically market participants such as traders, analysts and financial information providers.

The peculiarity of this dataset is that each contributor can define the sentiment of their tweets by labelling them as ‘bullish’ or ‘bearish’. The authors utilise this feature, which makes this study different from others which employ more complex textual analysis techniques.

The authors found a statistical relation between positive sentiment on stocks and their future positive return and have documented this factor as distinct from the five proposed by Fama-French.

In terms of the econometric rigour, this study is an improvement over prior ones but still lacks a long time series. It analysed data from 2013 to 2015 and was limited to a group of 15 US-based stocks.

News flash

A longer data set was studied by Stephen Heston from the University of Maryland and Nitish Sinha from the Federal Reserve in Washington. Their paper, titled ‘News versus Sentiment: Predicting Stock Returns from News Stories’, was published last autumn in the Financial Analyst Journal.

Their study brings a few improvements: it expands the time series from 2003 to 2010 and it explores the effect of aggregating news over horizons longer than one day, as well as the importance of understanding the tone of the news.

The authors found that daily aggregation of news sentiment is sub-optimal for predicting future stock returns. It is better to quantify the sentiment over at least a weekly period. They also found that news tone matters. In fact, negative news had the highest predictability.

The bottom line

In terms of the application of these new data sets by investment managers, private conversations I’ve had with some quantitative asset managers reveal an increased interest in studying them but caution in allocating a risk budget to these newer alpha signals.

In the words of Fan et al. (2014), ‘Big Data bring new opportunities to modern society but challenges to data scientists’.

According to the authors, the challenges brought by the high dimensionality of Big Data include: noise accumulation and spurious correlations; and heavy computational costs and algorithm instability.

There are interesting implications for investors but lot of more research work by the PhDs is needed.

Il termometro dei mercati finanziari (22 giugno 2018)
a cura di Emilio Barucci e Daniele Marazzina

Termometro

Giu 22 2018
Il termometro dei mercati finanziari (22 giugno 2018) a cura di Emilio Barucci e Daniele Marazzina

Continua l’iniziativa di Finriskalert.it “Il termometro dei mercati finanziari”. Questa rubrica vuole presentare un indicatore settimanale sul grado di turbolenza/tensione dei mercati finanziari con particolare attenzione all’Italia.

Significato degli indicatori

  • Rendimento borsa italiana: rendimento settimanale dell’indice della borsa italiana FTSEMIB;
  • Volatilità implicita borsa italiana: volatilità implicita calcolata considerando le opzioni at-the-money sul FTSEMIB a 3 mesi;
  • Future borsa italiana: valore del future sul FTSEMIB;
  • CDS principali banche 10Ysub: CDS medio delle obbligazioni subordinate a 10 anni delle principali banche italiane (Unicredit, Intesa San Paolo, MPS, Banco BPM);
  • Tasso di interesse ITA 2Y: tasso di interesse costruito sulla curva dei BTP con scadenza a due anni;
  • Spread ITA 10Y/2Y : differenza del tasso di interesse dei BTP a 10 anni e a 2 anni;
  • Rendimento borsa europea: rendimento settimanale dell’indice delle borse europee Eurostoxx;
  • Volatilità implicita borsa europea: volatilità implicita calcolata sulle opzioni at-the-money sull’indice Eurostoxx a scadenza 3 mesi;
  • Rendimento borsa ITA/Europa: differenza tra il rendimento settimanale della borsa italiana e quello delle borse europee, calcolato sugli indici FTSEMIB e Eurostoxx;
  • Spread ITA/GER: differenza tra i tassi di interesse italiani e tedeschi a 10 anni;
  • Spread EU/GER: differenza media tra i tassi di interesse dei principali paesi europei (Francia, Belgio, Spagna, Italia, Olanda) e quelli tedeschi a 10 anni;
  • Euro/dollaro: tasso di cambio euro/dollaro;
  • Spread US/GER 10Y: spread tra i tassi di interesse degli Stati Uniti e quelli tedeschi con scadenza 10 anni;
  • Prezzo Oro: quotazione dell’oro (in USD)
  • Spread 10Y/2Y Euro Swap Curve: differenza del tasso della curva EURO ZONE IRS 3M a 10Y e 2Y;
  • Euribor 6M: tasso euribor a 6 mesi.

I colori sono assegnati in un’ottica VaR: se il valore riportato è superiore (inferiore) al quantile al 15%, il colore utilizzato è l’arancione. Se il valore riportato è superiore (inferiore) al quantile al 5% il colore utilizzato è il rosso. La banda (verso l’alto o verso il basso) viene selezionata, a seconda dell’indicatore, nella direzione dell’instabilità del mercato. I quantili vengono ricostruiti prendendo la serie storica di un anno di osservazioni: ad esempio, un valore in una casella rossa significa che appartiene al 5% dei valori meno positivi riscontrati nell’ultimo anno. Per le prime tre voci della sezione “Politica Monetaria”, le bande per definire il colore sono simmetriche (valori in positivo e in negativo). I dati riportati provengono dal database Thomson Reuters. Infine, la tendenza mostra la dinamica in atto e viene rappresentata dalle frecce: ↑,↓, ↔  indicano rispettivamente miglioramento, peggioramento, stabilità.

Disclaimer: Le informazioni contenute in questa pagina sono esclusivamente a scopo informativo e per uso personale. Le informazioni possono essere modificate da finriskalert.it in qualsiasi momento e senza preavviso. Finriskalert.it non può fornire alcuna garanzia in merito all’affidabilità, completezza, esattezza ed attualità dei dati riportati e, pertanto, non assume alcuna responsabilità per qualsiasi danno legato all’uso, proprio o improprio delle informazioni contenute in questa pagina. I contenuti presenti in questa pagina non devono in alcun modo essere intesi come consigli finanziari, economici, giuridici, fiscali o di altra natura e nessuna decisione d’investimento o qualsiasi altra decisione deve essere presa unicamente sulla base di questi dati.

Il termometro dei mercati finanziari (15 giugno 2018)
a cura di Emilio Barucci e Daniele Marazzina

Termometro

Giu 16 2018
Il termometro dei mercati finanziari (15 giugno 2018) a cura di Emilio Barucci e Daniele Marazzina

Continua l’iniziativa di Finriskalert.it “Il termometro dei mercati finanziari”. Questa rubrica vuole presentare un indicatore settimanale sul grado di turbolenza/tensione dei mercati finanziari con particolare attenzione all’Italia.

Significato degli indicatori

  • Rendimento borsa italiana: rendimento settimanale dell’indice della borsa italiana FTSEMIB;
  • Volatilità implicita borsa italiana: volatilità implicita calcolata considerando le opzioni at-the-money sul FTSEMIB a 3 mesi;
  • Future borsa italiana: valore del future sul FTSEMIB;
  • CDS principali banche 10Ysub: CDS medio delle obbligazioni subordinate a 10 anni delle principali banche italiane (Unicredit, Intesa San Paolo, MPS, Banco BPM);
  • Tasso di interesse ITA 2Y: tasso di interesse costruito sulla curva dei BTP con scadenza a due anni;
  • Spread ITA 10Y/2Y : differenza del tasso di interesse dei BTP a 10 anni e a 2 anni;
  • Rendimento borsa europea: rendimento settimanale dell’indice delle borse europee Eurostoxx;
  • Volatilità implicita borsa europea: volatilità implicita calcolata sulle opzioni at-the-money sull’indice Eurostoxx a scadenza 3 mesi;
  • Rendimento borsa ITA/Europa: differenza tra il rendimento settimanale della borsa italiana e quello delle borse europee, calcolato sugli indici FTSEMIB e Eurostoxx;
  • Spread ITA/GER: differenza tra i tassi di interesse italiani e tedeschi a 10 anni;
  • Spread EU/GER: differenza media tra i tassi di interesse dei principali paesi europei (Francia, Belgio, Spagna, Italia, Olanda) e quelli tedeschi a 10 anni;
  • Euro/dollaro: tasso di cambio euro/dollaro;
  • Spread US/GER 10Y: spread tra i tassi di interesse degli Stati Uniti e quelli tedeschi con scadenza 10 anni;
  • Prezzo Oro: quotazione dell’oro (in USD)
  • Spread 10Y/2Y Euro Swap Curve: differenza del tasso della curva EURO ZONE IRS 3M a 10Y e 2Y;
  • Euribor 6M: tasso euribor a 6 mesi.

I colori sono assegnati in un’ottica VaR: se il valore riportato è superiore (inferiore) al quantile al 15%, il colore utilizzato è l’arancione. Se il valore riportato è superiore (inferiore) al quantile al 5% il colore utilizzato è il rosso. La banda (verso l’alto o verso il basso) viene selezionata, a seconda dell’indicatore, nella direzione dell’instabilità del mercato. I quantili vengono ricostruiti prendendo la serie storica di un anno di osservazioni: ad esempio, un valore in una casella rossa significa che appartiene al 5% dei valori meno positivi riscontrati nell’ultimo anno. Per le prime tre voci della sezione “Politica Monetaria”, le bande per definire il colore sono simmetriche (valori in positivo e in negativo). I dati riportati provengono dal database Thomson Reuters. Infine, la tendenza mostra la dinamica in atto e viene rappresentata dalle frecce: ↑,↓, ↔  indicano rispettivamente miglioramento, peggioramento, stabilità.

Disclaimer: Le informazioni contenute in questa pagina sono esclusivamente a scopo informativo e per uso personale. Le informazioni possono essere modificate da finriskalert.it in qualsiasi momento e senza preavviso. Finriskalert.it non può fornire alcuna garanzia in merito all’affidabilità, completezza, esattezza ed attualità dei dati riportati e, pertanto, non assume alcuna responsabilità per qualsiasi danno legato all’uso, proprio o improprio delle informazioni contenute in questa pagina. I contenuti presenti in questa pagina non devono in alcun modo essere intesi come consigli finanziari, economici, giuridici, fiscali o di altra natura e nessuna decisione d’investimento o qualsiasi altra decisione deve essere presa unicamente sulla base di questi dati.

ESMA reports on supervisory measures under EMIR

News

Giu 15 2018

The European Securities and Market Authority (ESMA) issued the first report on the supervisory measures and penalties  carried out under the  European Market Infrastructure Regulation (EMIR). The report focuses in particular on the supervisory actions undertaken national authorities, their supervisory powers and the interaction between these authorities and market participants when monitoring the compliance of the following EMIR requirements:

  • the clearing obligation for certain OTC derivatives (Art. 4 EMIR);
  • the reporting obligation of derivative transactions to TRs (Art. 9 EMIR);
  • requirements for non-financial counterparties (Art. 10 EMIR); and
  • Risk mitigation techniques for non-cleared OTC derivatives (Art. 11 EMIR).

ESMA has sent its report to the European Parliament, the Council and the Commission today, informing them about the findings, which will also help to gradually identifying best practices and potential areas that could benefit from a higher level of harmonisation.

Regarding the organization and allocation of competences related to the provisions in Articles 4, 9, 10 and 11 of EMIR, 14 countries (AT, CZ, DK, DE, FI, HU, IE, LV, MT, NO, PL, ES, SE, SK) have the supervisory powers and the power to impose penalties centralised in one single National Competent Authorities (NCA). It is observed that, among the countries with a single authority in charge of the supervision and the imposition of penalties, in 5 (AT, DK, FI, LV, SK.) out of 14, both the supervisory actions and the imposition of penalties are taken care by the same team/unit within the single authority.

On the contrary, the other 9 out of the 14 countries with a single authority there is a clear separation between the teams involved. In some NCAs, such as in the case of Germany and Ireland, the supervisory function is also split depending on the type of counterparty or on the specific provisions that are being monitored.

In respect to the other twelve countries (out of 26) that have the supervisory powers and the power to impose penalties decentralised and split between different NCAs, we observe that the majority of them share these competences with their respective Central Banks (with the exception of LX, IT, PT, SJ and SK).

In IE, sectoral supervision teams are responsible for supervising different entities’ compliance with all applicable legislation (including EMIR). The team responsible for supervising funds is also responsible for monitoring non-financial counterparties. In DE, one team focuses on matters related to Arts. 4, 10-11 and the other, to art. 9 of EMIR. In Italy, besides the role of BdI, Covip and IVASS are responsible respectively for the regulatory surveillance of pension funds and insurances.

The data gathered from the survey sheds some light on the level of interaction and the means used by the authorities to interact with market participants in relation to the implementation or the phase-in of EMIR provisions (in particular, Articles 4, 9 and 11 of EMIR).

The authorities have engaged in different activities aimed at providing awareness, training and guidance. In the majority of the 26 countries, authorities have engaged directly with market participants through different initiatives. Around 54% have launched processes to get feedback during the process of the EMIR implementation, with similar figures in respect to the clearing and the risk mitigation techniques and a higher percentage with respect to the reporting obligation. Around 58% of the NCAs have prepared specific trainings. In addition, 35% of the NCAs have engaged in working groups with market participants’ representatives.

Regarding the clearing obligation (Article 4 of EMIR), in Austria, Germany and Italy, authorities held trainings on intragroup transactions exemptions and the corresponding notifications. In Malta, three training sessions were organised for market participants (one with the participation of ESMA staff), focused on the clearing obligation, the intragroup exemptions regime and clearing obligation as applicable to financial and non-financial counterparties. In some countries, such as Belgium , trainings were addressed to independent auditors, who under the national law are responsible for checking the compliance of some entities with the provisions in Articles 4, 9 and 10 of EMIR. Another method used by some NCAs to interact with market participants is to establish working groups with representatives of market participants. In total, around 35% of the NCAs set up working groups in relation to Articles 4, 9 and 11 of EMIR44.

The report serves as a good basis for NCAs to share on their practices in their supervisory activities and more broadly, to raise awareness on the supervisory approaches followed in the different countries. It helps understand the information checked by NCAs and its use, for a range of supervisory measures.

The report also shows that the majority of NCAs share similar competences in their supervision and enforcement of Articles 4, 9, 10 and 11 of EMIR. ESMA expects this first report to be the baseline for future reports on penalties and supervisory measures, which will help monitor compliance in the different member states and possibly identify areas where a higher level of harmonisation could be considered to ensure a level playing field.

Supervisory Measures and Penalties under Articles 4, 9, 10 and 11 of EMIR (PDF)

FSB: RegTech, cryptocurrencies and sovereign risk

News

Giu 15 2018

The regional consulting group for Americas of the Financial Stability Board (FSB) met in Nassau to discuss economic development in the regions.

The economies in the Americas have better fundamentals than at the time of the 2013, some vulnerabilities have worsened, especially the overall leverage in the economy. The underlying fragilities in the region are the increased reliance on external funding and the high levels of debt, both private and public, in an environment of global recovery, inflation returning toward targets, and financial tightening.

The regulatory treatment of sovereign exposures has also be discussed. Namely, it was discussed how to monitor the risks that sovereign exposures play in the banking system, financial markets and the broader economy. The discussion followed a of the Basel Committee (The Regulatory Treatment of Sovereign Exposures, link below).

A rather new issue is that concerning the role of FinTech and RegTech in the improvement of the effective implementation of measures related to anti-money laundering and countering the financing of terrorism. Money laundering and terrorist financing risks are a concern in certain areas of the FSB’s work, including the potential financial stability implications of crypto-assets.

The discussion took place more broadly on how crypto-assets may have an impact on the financial landscape and potential implications for financial stability (although it was recognized that their size is still small relative to the overall financial system). Members also exchanged views on other regulatory aspects involved with crypto-assets and the role of central banks and financial regulators, given the rapid growth of crypto-asset markets and the growing involvement of retail investors.

 

FSB Americas Press Release (PDF)

 Basel Committee – The Regulatory Treatment of Sovereign Exposures (PDF)

Banca d’Italia: modiche alla regolamentazione dei covered bonds

News

Giu 15 2018

Il 14 giugno, la Banca d’Italia ha posto in pubblica consultazione la revisione della disciplina delle obbligazioni bancarie garantite (OBG). Prima di queste modifiche, l’emissione di OBG (covered bonds) era consentita ai gruppi bancari aventi, al momento dell’emissione, i seguenti requisiti:

  • fondi propri non inferiori a 250 milioni di euro; e
  • un total capital ratio a livello consolidato non inferiore al 9%.

Le modifiche permettono l’emissione di OBG anche alle banche che detengono fondi propri inferiori alla soglia di 250 milioni di euro. L’emissione è soggetta a una preventiva valutazione, caso per caso, condotta dalla Banca d’Italia e basata su alcuni elementi chiave:

  • gli obiettivi perseguiti attraverso l’emissione, i rischi connessi e l’impatto sugli equilibri economico-patrimoniali della banca attuali e prospettici;
  • l’adeguatezza delle policy, dei meccanismi di gestione dei rischi e delle procedure organizzative e di controllo volte ad assicurare l’ordinato e sicuro svolgimento del programma di emissione anche in caso di insolvenza o risoluzione, in specie con riferimento al rispetto dei requisiti organizzativi e dei presìdi previsti dal paragrafo 5;
  • l’adeguatezza delle competenze professionali in materia di obbligazioni garantite sviluppate dal personale responsabile dell’amministrazione e dei controlli sul programma;
  • il rispetto dei limiti alla cessione degli attivi idonei di cui al paragrafo 2;
  • la conformità alle disposizioni riguardanti la composizione del patrimonio separato e il rapporto minimo di collateralizzazione previste dal decreto del Ministro dell’economia e delle finanze del 14 dicembre 2006, n. 310.

Per le banche che detengono fondi propri in misura almeno pari a 250 milioni di euro rimangono ferme le attuali previsioni, che consentono di emettere OBG senza una comunicazione preventiva alla Banca d’Italia.

Disciplina delle OBG (PDF)

Revisione della disciplina delle OBG (PDF)

 

ECB announces the end of QE

News

Giu 15 2018

The Governing Council of the ECB met in Riga the 14th of June to review the cross-country pattern  towards a sustained adjustment of inflation. The discussion was supported by the latest Eurosystem staff macroeconomic projections, measures of price and wage pressures, and uncertainties surrounding the inflation outlook. Based on this review the Governing Council made the following decisions:

The Governing Council will continue to make net purchases under the asset purchase programme (APP) at the current monthly pace of €30 billion until the end of September 2018. After September 2018, subject to incoming data confirming the Governing Council’s medium-term inflation outlook, the monthly pace of the net asset purchases will be reduced to €15 billion until the end of December 2018 and that net purchases will then end.

The Governing Council intends to maintain its policy of reinvesting the principal payments from maturing securities purchased under the APP for an extended period of time after the end of the net asset purchases, and in any case for as long as necessary to maintain favourable liquidity conditions and an ample degree of monetary accommodation.

The Governing Council decided that the interest rate on the main refinancing operations and the interest rates on the marginal lending facility and the deposit facility will remain unchanged at 0.00%, 0.25% and -0.40% respectively. The Governing Council expects the key ECB interest rates to remain at their present levels at least through the summer of 2019 and in any case for as long as necessary to ensure that the evolution of inflation remains aligned with the current expectations of a sustained adjustment path.

Monetary policy decisions – Press release (HTML)

InsurTech – Insurance disrupted through Exponential Technologies
a cura di Deloitte Italia

Articolo

Giu 15 2018
InsurTech – Insurance disrupted through Exponential Technologies a cura di Deloitte Italia

Digital Insurance Ecosystem – Disruptive Technologies and Innovation

Technology is transforming the insurance industry requiring a new niche of insurance products and services. Insurers will need a laser focus on how they will remain relevant, as well as profitable, in an increasingly tech-centric and connected society. It is crucial to assess disruption across the insurance ecosystem and determines how it affects the whole environment.

Disruption

Disruptive innovation describes a process by which a product or service takes root initially in simple applications at the bottom of a market and then relentlessly moves up market, eventually displacing established competitors.

Insurers through accelerator programs aimed specifically at insurance tech entrepreneurs along with enthusiasts and accessible talent could unwind vast potential in InsurTech.

The insurance industry is perhaps facing more disruption than any other industry. Many incumbent players feel the exponential growth of digitization posing threats to their industry, particularly the entry of innovative firms or FinTech / InsurTech, which is an economic industry, composed of companies that use technology to make financial services more efficient and the purpose is to disrupt incumbent financial systems and corporations that rely less on software.

Examples of disruptive technology includes Fintech/InsurTech, Robotics, Cognitive Automation, Robo-Agents/Advisors, Chatbot.

Analytics

Organizations of all sizes are seeking to master, monetize, and measure their use of data. Business analytics specialists look inside this data to help create and refine strategies for delivering data-driven insights that yield informed and differentiating business decisions.

Business analytics services also provide customized data analytics tools that are ready for deployment to immediately improve an organization’s analytics capabilities.

Building on analytics successes, leaders are beginning to take steps toward connecting these successes to create the insight-driven organization.

The main areas that can be improved are Predictive Analytics, Customer Analytics, Operational Analytics, Big Data, and Advanced Analytics.

Internet of Things

The Internet of Things (IoT) is arguably one of the hottest technology trends of today. This refers to a world of intelligent, connected devices that generate data for automating business processes and enabling new services.

Experts believe that the insurance industry will undergo a marked change with the growing adoption of IoT.

New business models, revenue streams and prospects will emerge. Functions core to the industry including risk assessment, sales processes for insurance products will be reinvented. Partnerships with smart device manufacturers, analytics providers, telecom players, software firms and even competitors will enable insurers to create competitive advantages, new revenue sources and effective, innovative business models.

Examples: Personal wearables, Smarthomes, Smart-businesses, Telematics and black boxes, IoT-based coverage

Mobility

Mobility is more than just the latest step function in tech innovation. It is a fast-moving engine that is fundamentally reshaping operating models, business models, and marketplaces. Mobility also includes GPS enabled programs, mobile apps and online market places linking insurers with customers.

Insurers are driving mobility transformation in their businesses by identifying use cases that can be mobile enabled. Utilizing the true power of mobility requires insurers to enable speedy, high quality communications for customers, field agents, and the management.

Firms that proactively adopt mobile technology enable customers to do business on-the-fly and are seeing improvement in customer loyalty.

Mobility also helps the enterprise provide rich on-site data to employees including claims adjusters besides powerful decision making tools to the management.

The future of Mobility, which would upend the existing models of insurance, includes Sensor-controlled cars, Self-driving cars, Mobile apps, Tablet/Mobile based Solutions and Drones.

Tech Transformation

Technology transformation is not just about upgrading, it is about establishing the right portfolio of technology to run the business most effectively.

Tech Transformation incorporates client issues and sub-issues around digital and emerging technology (cloud, social computing), technology Initiatives, cyber risk and comparison websites.

It also recognizes promising commercial potentials in bringing forth efficiencies in current products as well as in new markets opportunities.

Digital technologies, such as social media and telematics, will continue to transform the systems insurers, reinsurers and brokers use. The connected world will alter the insurance market landscape throughout its value chain. It is imperative insurers identify tech trends, plan, partner and react fast by incorporating innovation into the enterprise culture in order to survive and thrive.

The main important trends include Artificial Intelligence, Machine Learning Tools, Automated underwriting/pricing, Cloud Computing and Digital Technologies.

New emerging paradigm: the Transformation of the Actuarial Profession

As this shift unfold, the actuarial professional is changing dramatically – opening opportunities for actuaries to take on dynamic, new business roles.

A wider range of actuarial tasks, empowering actuaries to shift their focus to higher value activities are encompassing more resources. This pivot towards more advanced strategic analysis – requiring sophisticated cognitive ability, communications savvy, and business knowledge – is the engine driving the rise of the shift in the Actuarial Profession.

These new technologies are transforming actuarial tasks in fundamental ways:

Facilitating data gathering and preparation – Technologies efficiently prepare data for analysis, including finding, cleaning, organizing, and parsing data. In the future, actuaries will spend less time on these manual process tasks and more time generating insights that drive business performance.

Performing analysis and computation – Technologies are programmed to perform rote calculations that, while complicated, require lower cognitive skills. This area has seen the most automation to date, and the use of these powerful, brute force-computing tools will only become more powerful as models are consolidated, refined, span a wider spectrum of actuarial processes, and are shared across user groups.

Improving reporting and analytics – Technologies can automate actuarial reporting based on rule sets, machine learning, and natural language generation capabilities. This will enable actuaries to focus on fine‑turning reports, developing insights from data, and communicating these insights to business leaders.

Defining the Actuary of the Future

While some express uncertainty about these shifts, it should be seen a once‑in‑a‑lifetime opportunity.

Actuaries who embrace this change will elevate their capabilities and strengthen their value to their organizations.

It starts with a shift in mindset from data steward to business strategist. Rather than simply producing numbers, actuaries must learn how to harness data to generate business insights, serving as the organization’s bridge between technology and strategy.

To make this jump, actuaries must augment their fluency with numbers with a deeper understanding of the business. This means applying actuarial skills not simply to traditional tasks such as compliance and risk management, but to broader challenges:

  • Analyzing the market challenges their organizations face.
  • Helping organizations decide what products to sell and the best channels to distribute products.
  • Providing insights about profitability, maximizing returns for shareholders, and entering the right markets at the right prices.

Giovanni Di Marco – Partner Deloitte Consulting | Actuarial, Rewards and Analytics